Hvac system with automated device pairing

ABSTRACT

A heating, ventilation, and air conditioning (HVAC) system for a building includes a plurality of actuation devices operable to affect one or more variables in the building, a plurality of sensors configured to measure the variables affected by the actuation devices, and a controller. The controller is configured to operate the actuation devices to affect one or more of the measured variables by providing an actuation signal to the actuation devices and to receive sensor response signals from the sensors. The sensor response signals indicate an effect of the actuation signal on the measured variables. For each of the sensor response signals, the controller is configured to calculate a similarity metric indicating a similarity between the sensor response signal and the actuation signal. The controller is configured to automatically establish a device pairing including one of the actuation devices and one of the sensors based on the similarity metrics.

CROSS-REFERENCE TO RELATED PATENT APPLICATION

This application is a continuation of U.S. patent application Ser. No.15/400,926 filed Jan. 6, 2017, the entirety of which is incorporated byreference herein.

BACKGROUND

The present disclosure relates generally to heating, ventilation, andair conditioning (HVAC) systems for a building. The present disclosurerelates more particularly to systems and methods for automaticallyestablishing relationships between sensors and actuation devices in abuilding HVAC system.

Building HVAC systems typically include many different sensors andactuation devices. The sensors measure various environmental variables(e.g., temperature, humidity, air flow, etc.) and provide sensorreadings to a controller. In a feedback control system, a controlleruses the sensor readings to generate appropriate control signals for theactuation devices (e.g., chillers, boilers, valves, actuators, etc.)which operate to affect the environmental variables measured by thesensors. Some buildings have hundreds of sensors and actuation devices.It can be cumbersome, error prone, and time consuming to manuallyidentify the associations between sensors and their correspondingactuation devices. Additionally, buildings evolve over time which canchange existing relationships between sensors and actuation devices. Forexample, remodeling a building can break existing relationships betweenpaired sensors and actuation devices or create new relationships thatdid not previously exist.

Some building HVAC systems record time series data (e.g., trend data,historical data, etc.) for various measured or calculated variables.Time series data can be used for many purposes including, for example,fault detection, benchmarking, executing queries, and other dataanalysis applications. Some data analysis applications compare two ormore time series as part of the analysis. However, it can be difficultto compare time series to each other due to dimensional mismatchesresulting from different sampling rates, different units of measurement,and other factors. Without the ability to accurately compare time seriesdata, performing actions such as finding similar trends, executingqueries on a broad data set, running diagnostic algorithms, conductingbenchmarking, reporting on compliance across portfolio of systems orbuildings, and other actions can be challenging.

SUMMARY

One implementation of the present disclosure is a heating, ventilation,and air conditioning (HVAC) system for a building. The HVAC systemincludes a number of actuation devices operable to affect one or morevariables in the building, a number of sensors configured to measure thevariables affected by the actuation devices, and a controller. Thecontroller is configured to operate the actuation devices to affect oneor more of the measured variables by providing an actuation signal tothe actuation devices and to receive sensor response signals from thesensors. The sensor response signals indicate an effect of the actuationsignal on the measured variables. For each of the sensor responsesignals, the controller is configured to calculate a similarity metricindicating a similarity between the sensor response signal and theactuation signal. The controller is configured to automaticallyestablish a device pairing including one of the actuation devices andone of the sensors based on the similarity metrics.

In some embodiments, the device pairing defines a control relationshipbetween the actuation device in the device pairing and the sensor in thedevice pairing. The control relationship can indicate that the actuationdevice in the device pairing is operable to control a variable measuredby the sensor in the device pairing.

In some embodiments, the controller is configured to automaticallycreate a feedback control loop including the actuation device in thedevice pairing and the sensor in the device pairing. The controller canuse the feedback control loop to generate and provide control signals tothe actuation device in the device pairing based on measurementsreceived from the sensor in the device pairing.

In some embodiments, the controller is configured to calculate thesimilarity metrics based on differences between samples of the actuationsignal and corresponding samples of each of the sensor response signals.

In some embodiments, the controller is configured to determine, for eachof the sensor response signals, a delay time of the sensor responsesignal relative to the actuation signal. The controller can identify asensor corresponding to the sensor response signal having a minimum ofthe delay times and can establish the device pairing such that theidentified sensor is included in the device pairing.

In some embodiments, the controller is configured to generate anactuation signal time series including a plurality of samples of theactuation signal and generate a sensor response time series for each ofthe sensor response signals. Each sensor response time series mayinclude a plurality of samples of one of the measured variables. In someembodiments, the controller calculates the similarity metrics bycomparing the actuation signal time series to each of the sensorresponse time series.

In some embodiments, the controller is configured to detect adimensional mismatch between the actuation signal time series and one ormore of the sensor response time series and correct the dimensionalmismatch by modifying at least one of the actuation signal time seriesand one or more of the sensor response time series.

In some embodiments, the controller is configured to apply a discretecosine transformation (DCT) to the actuation signal and each of thesensor response signals. Each DCT may generate a plurality of DCTcoefficients. The controller can calculate the similarity metrics bycomparing the DCT coefficients resulting from the DCT of the actuationsignal to DCT coefficients resulting from the DCT of each sensorresponse signal.

In some embodiments, the controller is configured to receive baselinesensor signals from each of the plurality of sensors. The baselinesensor signals may indicate values of the measured variables during atime period before the actuation signal is provided to the actuationdevices. For each of the baseline sensor signals, the controller cancalculate a similarity metric indicating a similarity between thebaseline sensor signal and the actuation signal.

In some embodiments, the controller is configured to determine, for eachof the plurality of sensors, whether the similarity metric calculatedbased on the sensor response signal indicates a greater similarity thanthe similarity metric calculated based on the baseline sensor signal. Insome embodiments, the controller establishes the device pairing inresponse to a determination that the similarity metric calculated basedon the sensor response signal indicates a greater similarity than thesimilarity metric calculated based on the baseline sensor signal.

Another implementation of the present disclosure is a method forestablishing device pairings in a heating, ventilation, and airconditioning (HVAC) system for a building. The method includes operatingone or more actuation devices to affect one or more measured variablesin the building by providing an actuation signal to the actuationdevices and receiving sensor response signals from a plurality ofsensors configured to measure the variables affected by the actuationdevices. The sensor response signals indicate an effect of the actuationsignal on the measured variables. The method includes calculating asimilarity metric for each of the sensor response signals. Eachsimilarity metric indicates a similarity between the actuation signaland one of the sensor response signals. The method includesautomatically establishing a device pairing including one of theactuation devices and one of the sensors based on the similaritymetrics.

In some embodiments, the device pairing defines a control relationshipbetween the actuation device in the device pairing and the sensor in thedevice pairing. The control relationship may indicate that the actuationdevice in the device pairing is operable to control a variable measuredby the sensor in the device pairing.

In some embodiments, the method includes automatically creating afeedback control loop including the actuation device in the devicepairing and the sensor in the device pairing. The method may includeusing the feedback control loop to generate and provide control signalsto the actuation device in the device pairing based on measurementsreceived from the sensor in the device pairing.

In some embodiments, the similarity metrics are calculated based ondifferences between samples of the actuation signal and correspondingsamples of each of the sensor response signals.

In some embodiments, the method includes determining, for each of thesensor response signals, a delay time of the sensor response signalrelative to the actuation signal. The method may include identifying asensor corresponding to the sensor response signal having a minimum ofthe delay times and establishing the device pairing such that theidentified sensor is included in the device pairing.

In some embodiments, the method includes generating an actuation signaltime series including a plurality of samples of the actuation signal andgenerating a sensor response time series for each of the sensor responsesignals. Each sensor response time series may include a plurality ofsamples of one of the measured variables. The method may includecalculating the similarity metrics by comparing the actuation signaltime series to each of the sensor response time series.

In some embodiments, the method includes detecting a dimensionalmismatch between the actuation signal time series and one or more of thesensor response time series and correcting the dimensional mismatch bymodifying at least one of the actuation signal time series and one ormore of the sensor response time series.

In some embodiments, the method includes applying a discrete cosinetransformation (DCT) to the actuation signal and each of the sensorresponse signals. Each DCT may generate a plurality of DCT coefficients.The method may include calculating the similarity metrics by comparingthe DCT coefficients resulting from the DCT of the actuation signal toDCT coefficients resulting from the DCT of each sensor response signal.

In some embodiments, the method includes receiving baseline sensorsignals from each of the plurality of sensors. The baseline sensorsignals may indicate values of the measured variables during a timeperiod before the actuation signal is provided to the actuation devices.The method may include, for each of the baseline sensor signals,calculating a similarity metric indicating a similarity between thebaseline sensor signal and the actuation signal.

In some embodiments, the method includes determining, for each of theplurality of sensors, whether the similarity metric calculated based onthe sensor response signal indicates a greater similarity than thesimilarity metric calculated based on the baseline sensor signal. Themethod may include establishing the device pairing in response to adetermination that the similarity metric calculated based on the sensorresponse signal indicates a greater similarity than the similaritymetric calculated based on the baseline sensor signal.

Those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, inventive features, and advantages of the devices and/orprocesses described herein, as defined solely by the claims, will becomeapparent in the detailed description set forth herein and taken inconjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments will become more fully understood from the followingdetailed description, taken in conjunction with the accompanyingdrawings, wherein like reference numerals refer to like elements, inwhich:

FIG. 1 is a drawing of a building equipped with a heating, ventilation,and air conditioning (HVAC) system, according to an exemplaryembodiment;

FIG. 2 is a drawing of a waterside system which can be used incombination with the HVAC system of FIG. 1, according to an exemplaryembodiment;

FIG. 3 is a drawing of an airside system which can be used incombination with the HVAC system of FIG. 1, according to an exemplaryembodiment;

FIG. 4 is a block diagram of a building management system which can beused to monitor and control the building and HVAC system of FIG. 1,according to an exemplary embodiment;

FIG. 5 is a block diagram of another building management system whichcan be used to monitor and control the building and HVAC system of FIG.1, according to an exemplary embodiment;

FIG. 6A is a block diagram of a HVAC system including sensors, actuationdevices, and a controller, which can be implemented in the building ofFIG. 1, according to an exemplary embodiment;

FIG. 6B is a block diagram illustrating a portion of the HVAC system ofFIG. 6A in greater detail including a smart actuator which can beconfigured to perform automated device pairing, according to anexemplary embodiment;

FIG. 6C is a block diagram illustrating a portion of the HVAC system ofFIG. 6A in greater detail including a smart chiller which can beconfigured to perform automated device pairing, according to anexemplary embodiment;

FIG. 6D is a block diagram illustrating a portion of the HVAC system ofFIG. 6A in greater detail including a smart thermostat which can beconfigured to perform automated device pairing, according to anexemplary embodiment;

FIG. 7 is a graph illustrating different types of signals and timeseries evaluated by the controller of FIG. 6A, according to an exemplaryembodiment;

FIG. 8 is a graph illustrating dimensional mismatch handling performedby the controller of FIG. 6A, according to an exemplary embodiment;

FIG. 9 is a flowchart of a process for establishing device pairingsbetween sensors and actuation devices, which can be performed by thecontroller of FIG. 6A, according to an exemplary embodiment; and

FIG. 10 is a flowchart of a process for handling dimensional mismatchesbetween actuation signal time series and sensor response time series,which can be performed by the controller of FIG. 6A, according to anexemplary embodiment.

DETAILED DESCRIPTION

Referring generally to the FIGURES, a heating, ventilation, and airconditioning (HVAC) system with automated device pairing and dimensionalmismatch handling are shown according to various exemplary embodiments.The HVAC system includes a plurality of sensors and actuation devices(e.g., chillers, boilers, fans, dampers, actuators, valves, etc.). Thesensors measure various environmental variables in the building (e.g.,zone temperature, humidity, air flow, etc.). The actuation devicesoperate to affect the measured variables by providing heating, cooling,airflow, etc. to the building. A controller provides actuation signalsto the actuation devices and receives sensor response signals from thesensors. The controller uses the sensor response signals to determine aneffect of the actuation signals on the measured variables.

In some embodiments, the controller automatically establishes devicepairings between sensors and actuation devices based on the sensorresponse signals. For each combination of an actuation signal and asensor response signal, the controller can calculate a similaritymetric. The similarity metric indicates the similarity or closenessbetween the sensor response signal and the actuation signal. Thecontroller can use the similarity metrics to identify which of thesensor response signals most closely matches each actuation signal. Thecontroller can then establish a device pairing between the actuationdevice and the sensor corresponding to the matching actuation signal andsensor response signal.

In some embodiments, the controller stores time series data for theactuation signals and sensor response signals. Different variables canbe measured at different sampling rates, which can lead do dimensionalmismatches between two or more time series that span the same range oftimes. For example, a time series sampled at a rate often samples persecond may include twice the number of samples as a different timeseries sampled at a rate of five samples per second. The controller canautomatically handle dimensional mismatches between two or more timeseries by performing a discrete cosine transformation for each timeseries.

A discrete cosine transformation (DCT) expresses a finite sequence ofdata points in terms of a sum of cosine functions oscillating atdifferent frequencies. Performing the DCT may result in a set of DCTcoefficients for each time series. The DCT coefficients represent themagnitudes of the cosine functions in the summation. The controller canapply a quantization process to the DCT coefficients in each set suchthat only a predetermined number of the DCT coefficients in each set areretained. The remaining DCT coefficients can be discarded or replacedwith zeros, which has the effect of removing some of the higherfrequency cosine functions from the summation. The controller cancompare two or more time series by comparing the DCT coefficientsresulting from each DCT. Advantageously, this allows for directcomparison between the transformed time series without requiringdecompression, interpolation, synchronization, or other processingsteps. Other features and advantages of the HVAC system and controllerare described in greater detail below.

Building HVAC Systems and Building Management Systems

Referring now to FIGS. 1-5, several building management systems (BMS)and HVAC systems in which the systems and methods of the presentdisclosure can be implemented are shown, according to some embodiments.In brief overview, FIG. 1 shows a building 10 equipped with a HVACsystem 100. FIG. 2 is a block diagram of a waterside system 200 whichcan be used to serve building 10. FIG. 3 is a block diagram of anairside system 300 which can be used to serve building 10. FIG. 4 is ablock diagram of a BMS which can be used to monitor and control building10. FIG. 5 is a block diagram of another BMS which can be used tomonitor and control building 10.

Building and HVAC System

Referring particularly to FIG. 1, a perspective view of a building 10 isshown. Building 10 is served by a BMS. A BMS is, in general, a system ofdevices configured to control, monitor, and manage equipment in oraround a building or building area. A BMS can include, for example, aHVAC system, a security system, a lighting system, a fire alertingsystem, any other system that is capable of managing building functionsor devices, or any combination thereof.

The BMS that serves building 10 includes a HVAC system 100. HVAC system100 can include a plurality of HVAC devices (e.g., heaters, chillers,air handling units, pumps, fans, thermal energy storage, etc.)configured to provide heating, cooling, ventilation, or other servicesfor building 10. For example, HVAC system 100 is shown to include awaterside system 120 and an airside system 130. Waterside system 120 mayprovide a heated or chilled fluid to an air handling unit of airsidesystem 130. Airside system 130 may use the heated or chilled fluid toheat or cool an airflow provided to building 10. An exemplary watersidesystem and airside system which can be used in HVAC system 100 aredescribed in greater detail with reference to FIGS. 2-3.

HVAC system 100 is shown to include a chiller 102, a boiler 104, and arooftop air handling unit (AHU) 106. Waterside system 120 may use boiler104 and chiller 102 to heat or cool a working fluid (e.g., water,glycol, etc.) and may circulate the working fluid to AHU 106. In variousembodiments, the HVAC devices of waterside system 120 can be located inor around building 10 (as shown in FIG. 1) or at an offsite locationsuch as a central plant (e.g., a chiller plant, a steam plant, a heatplant, etc.). The working fluid can be heated in boiler 104 or cooled inchiller 102, depending on whether heating or cooling is required inbuilding 10. Boiler 104 may add heat to the circulated fluid, forexample, by burning a combustible material (e.g., natural gas) or usingan electric heating element. Chiller 102 may place the circulated fluidin a heat exchange relationship with another fluid (e.g., a refrigerant)in a heat exchanger (e.g., an evaporator) to absorb heat from thecirculated fluid. The working fluid from chiller 102 and/or boiler 104can be transported to AHU 106 via piping 108.

AHU 106 may place the working fluid in a heat exchange relationship withan airflow passing through AHU 106 (e.g., via one or more stages ofcooling coils and/or heating coils). The airflow can be, for example,outside air, return air from within building 10, or a combination ofboth. AHU 106 may transfer heat between the airflow and the workingfluid to provide heating or cooling for the airflow. For example, AHU106 can include one or more fans or blowers configured to pass theairflow over or through a heat exchanger containing the working fluid.The working fluid may then return to chiller 102 or boiler 104 viapiping 110.

Airside system 130 may deliver the airflow supplied by AHU 106 (i.e.,the supply airflow) to building 10 via air supply ducts 112 and mayprovide return air from building 10 to AHU 106 via air return ducts 114.In some embodiments, airside system 130 includes multiple variable airvolume (VAV) units 116. For example, airside system 130 is shown toinclude a separate VAV unit 116 on each floor or zone of building 10.VAV units 116 can include dampers or other flow control elements thatcan be operated to control an amount of the supply airflow provided toindividual zones of building 10. In other embodiments, airside system130 delivers the supply airflow into one or more zones of building 10(e.g., via supply ducts 112) without using intermediate VAV units 116 orother flow control elements. AHU 106 can include various sensors (e.g.,temperature sensors, pressure sensors, etc.) configured to measureattributes of the supply airflow. AHU 106 may receive input from sensorslocated within AHU 106 and/or within the building zone and may adjustthe flow rate, temperature, or other attributes of the supply airflowthrough AHU 106 to achieve setpoint conditions for the building zone.

Waterside System

Referring now to FIG. 2, a block diagram of a waterside system 200 isshown, according to some embodiments. In various embodiments, watersidesystem 200 may supplement or replace waterside system 120 in HVAC system100 or can be implemented separate from HVAC system 100. Whenimplemented in HVAC system 100, waterside system 200 can include asubset of the HVAC devices in HVAC system 100 (e.g., boiler 104, chiller102, pumps, valves, etc.) and may operate to supply a heated or chilledfluid to AHU 106. The HVAC devices of waterside system 200 can belocated within building 10 (e.g., as components of waterside system 120)or at an offsite location such as a central plant.

In FIG. 2, waterside system 200 is shown as a central plant having aplurality of subplants 202-212. Subplants 202-212 are shown to include aheater subplant 202, a heat recovery chiller subplant 204, a chillersubplant 206, a cooling tower subplant 208, a hot thermal energy storage(TES) subplant 210, and a cold thermal energy storage (TES) subplant212. Subplants 202-212 consume resources (e.g., water, natural gas,electricity, etc.) from utilities to serve thermal energy loads (e.g.,hot water, cold water, heating, cooling, etc.) of a building or campus.For example, heater subplant 202 can be configured to heat water in ahot water loop 214 that circulates the hot water between heater subplant202 and building 10. Chiller subplant 206 can be configured to chillwater in a cold water loop 216 that circulates the cold water betweenchiller subplant 206 building 10. Heat recovery chiller subplant 204 canbe configured to transfer heat from cold water loop 216 to hot waterloop 214 to provide additional heating for the hot water and additionalcooling for the cold water. Condenser water loop 218 may absorb heatfrom the cold water in chiller subplant 206 and reject the absorbed heatin cooling tower subplant 208 or transfer the absorbed heat to hot waterloop 214. Hot TES subplant 210 and cold TES subplant 212 may store hotand cold thermal energy, respectively, for subsequent use.

Hot water loop 214 and cold water loop 216 may deliver the heated and/orchilled water to air handlers located on the rooftop of building 10(e.g., AHU 106) or to individual floors or zones of building 10 (e.g.,VAV units 116). The air handlers push air past heat exchangers (e.g.,heating coils or cooling coils) through which the water flows to provideheating or cooling for the air. The heated or cooled air can bedelivered to individual zones of building 10 to serve thermal energyloads of building 10. The water then returns to subplants 202-212 toreceive further heating or cooling.

Although subplants 202-212 are shown and described as heating andcooling water for circulation to a building, it is understood that anyother type of working fluid (e.g., glycol, CO2, etc.) can be used inplace of or in addition to water to serve thermal energy loads. In otherembodiments, subplants 202-212 may provide heating and/or coolingdirectly to the building or campus without requiring an intermediateheat transfer fluid. These and other variations to waterside system 200are within the teachings of the present disclosure.

Each of subplants 202-212 can include a variety of equipment configuredto facilitate the functions of the subplant. For example, heatersubplant 202 is shown to include a plurality of heating elements 220(e.g., boilers, electric heaters, etc.) configured to add heat to thehot water in hot water loop 214. Heater subplant 202 is also shown toinclude several pumps 222 and 224 configured to circulate the hot waterin hot water loop 214 and to control the flow rate of the hot waterthrough individual heating elements 220. Chiller subplant 206 is shownto include a plurality of chillers 232 configured to remove heat fromthe cold water in cold water loop 216. Chiller subplant 206 is alsoshown to include several pumps 234 and 236 configured to circulate thecold water in cold water loop 216 and to control the flow rate of thecold water through individual chillers 232.

Heat recovery chiller subplant 204 is shown to include a plurality ofheat recovery heat exchangers 226 (e.g., refrigeration circuits)configured to transfer heat from cold water loop 216 to hot water loop214. Heat recovery chiller subplant 204 is also shown to include severalpumps 228 and 230 configured to circulate the hot water and/or coldwater through heat recovery heat exchangers 226 and to control the flowrate of the water through individual heat recovery heat exchangers 226.Cooling tower subplant 208 is shown to include a plurality of coolingtowers 238 configured to remove heat from the condenser water incondenser water loop 218. Cooling tower subplant 208 is also shown toinclude several pumps 240 configured to circulate the condenser water incondenser water loop 218 and to control the flow rate of the condenserwater through individual cooling towers 238.

Hot TES subplant 210 is shown to include a hot TES tank 242 configuredto store the hot water for later use. Hot TES subplant 210 may alsoinclude one or more pumps or valves configured to control the flow rateof the hot water into or out of hot TES tank 242. Cold TES subplant 212is shown to include cold TES tanks 244 configured to store the coldwater for later use. Cold TES subplant 212 may also include one or morepumps or valves configured to control the flow rate of the cold waterinto or out of cold TES tanks 244.

In some embodiments, one or more of the pumps in waterside system 200(e.g., pumps 222, 224, 228, 230, 234, 236, and/or 240) or pipelines inwaterside system 200 include an isolation valve associated therewith.Isolation valves can be integrated with the pumps or positioned upstreamor downstream of the pumps to control the fluid flows in watersidesystem 200. In various embodiments, waterside system 200 can includemore, fewer, or different types of devices and/or subplants based on theparticular configuration of waterside system 200 and the types of loadsserved by waterside system 200.

Airside System

Referring now to FIG. 3, a block diagram of an airside system 300 isshown, according to some embodiments. In various embodiments, airsidesystem 300 may supplement or replace airside system 130 in HVAC system100 or can be implemented separate from HVAC system 100. Whenimplemented in HVAC system 100, airside system 300 can include a subsetof the HVAC devices in HVAC system 100 (e.g., AHU 106, VAV units 116,ducts 112-114, fans, dampers, etc.) and can be located in or aroundbuilding 10. Airside system 300 may operate to heat or cool an airflowprovided to building 10 using a heated or chilled fluid provided bywaterside system 200.

In FIG. 3, airside system 300 is shown to include an economizer-type airhandling unit (AHU) 302. Economizer-type AHUs vary the amount of outsideair and return air used by the air handling unit for heating or cooling.For example, AHU 302 may receive return air 304 from building zone 306via return air duct 308 and may deliver supply air 310 to building zone306 via supply air duct 312. In some embodiments, AHU 302 is a rooftopunit located on the roof of building 10 (e.g., AHU 106 as shown inFIG. 1) or otherwise positioned to receive both return air 304 andoutside air 314. AHU 302 can be configured to operate exhaust air damper316, mixing damper 318, and outside air damper 320 to control an amountof outside air 314 and return air 304 that combine to form supply air310. Any return air 304 that does not pass through mixing damper 318 canbe exhausted from AHU 302 through exhaust damper 316 as exhaust air 322.

Each of dampers 316-320 can be operated by an actuator. For example,exhaust air damper 316 can be operated by actuator 324, mixing damper318 can be operated by actuator 326, and outside air damper 320 can beoperated by actuator 328. Actuators 324-328 may communicate with an AHUcontroller 330 via a communications link 332. Actuators 324-328 mayreceive control signals from AHU controller 330 and may provide feedbacksignals to AHU controller 330. Feedback signals can include, forexample, an indication of a current actuator or damper position, anamount of torque or force exerted by the actuator, diagnosticinformation (e.g., results of diagnostic tests performed by actuators324-328), status information, commissioning information, configurationsettings, calibration data, and/or other types of information or datathat can be collected, stored, or used by actuators 324-328. AHUcontroller 330 can be an economizer controller configured to use one ormore control algorithms (e.g., state-based algorithms, extremum seekingcontrol (ESC) algorithms, proportional-integral (PI) control algorithms,proportional-integral-derivative (PID) control algorithms, modelpredictive control (MPC) algorithms, feedback control algorithms, etc.)to control actuators 324-328.

Still referring to FIG. 3, AHU 302 is shown to include a cooling coil334, a heating coil 336, and a fan 338 positioned within supply air duct312. Fan 338 can be configured to force supply air 310 through coolingcoil 334 and/or heating coil 336 and provide supply air 310 to buildingzone 306. AHU controller 330 may communicate with fan 338 viacommunications link 340 to control a flow rate of supply air 310. Insome embodiments, AHU controller 330 controls an amount of heating orcooling applied to supply air 310 by modulating a speed of fan 338.

Cooling coil 334 may receive a chilled fluid from waterside system 200(e.g., from cold water loop 216) via piping 342 and may return thechilled fluid to waterside system 200 via piping 344. Valve 346 can bepositioned along piping 342 or piping 344 to control a flow rate of thechilled fluid through cooling coil 334. In some embodiments, coolingcoil 334 includes multiple stages of cooling coils that can beindependently activated and deactivated (e.g., by AHU controller 330, byBMS controller 366, etc.) to modulate an amount of cooling applied tosupply air 310.

Heating coil 336 may receive a heated fluid from waterside system 200(e.g., from hot water loop 214) via piping 348 and may return the heatedfluid to waterside system 200 via piping 350. Valve 352 can bepositioned along piping 348 or piping 350 to control a flow rate of theheated fluid through heating coil 336. In some embodiments, heating coil336 includes multiple stages of heating coils that can be independentlyactivated and deactivated (e.g., by AHU controller 330, by BMScontroller 366, etc.) to modulate an amount of heating applied to supplyair 310.

Each of valves 346 and 352 can be controlled by an actuator. Forexample, valve 346 can be controlled by actuator 354 and valve 352 canbe controlled by actuator 356. Actuators 354-356 may communicate withAHU controller 330 via communications links 358-360. Actuators 354-356may receive control signals from AHU controller 330 and may providefeedback signals to controller 330. In some embodiments, AHU controller330 receives a measurement of the supply air temperature from atemperature sensor 362 positioned in supply air duct 312 (e.g.,downstream of cooling coil 334 and/or heating coil 336). AHU controller330 may also receive a measurement of the temperature of building zone306 from a temperature sensor 364 located in building zone 306.

In some embodiments, AHU controller 330 operates valves 346 and 352 viaactuators 354-356 to modulate an amount of heating or cooling providedto supply air 310 (e.g., to achieve a setpoint temperature for supplyair 310 or to maintain the temperature of supply air 310 within asetpoint temperature range). The positions of valves 346 and 352 affectthe amount of heating or cooling provided to supply air 310 by coolingcoil 334 or heating coil 336 and may correlate with the amount of energyconsumed to achieve a desired supply air temperature. AHU 330 maycontrol the temperature of supply air 310 and/or building zone 306 byactivating or deactivating coils 334-336, adjusting a speed of fan 338,or a combination of both.

Still referring to FIG. 3, airside system 300 is shown to include abuilding management system (BMS) controller 366 and a client device 368.BMS controller 366 can include one or more computer systems (e.g.,servers, supervisory controllers, subsystem controllers, etc.) thatserve as system level controllers, application or data servers, headnodes, or master controllers for airside system 300, waterside system200, HVAC system 100, and/or other controllable systems that servebuilding 10. BMS controller 366 may communicate with multiple downstreambuilding systems or subsystems (e.g., HVAC system 100, a securitysystem, a lighting system, waterside system 200, etc.) via acommunications link 370 according to like or disparate protocols (e.g.,LON, BACnet, etc.). In various embodiments, AHU controller 330 and BMScontroller 366 can be separate (as shown in FIG. 3) or integrated. In anintegrated implementation, AHU controller 330 can be a software moduleconfigured for execution by a processor of BMS controller 366.

In some embodiments, AHU controller 330 receives information from BMScontroller 366 (e.g., commands, setpoints, operating boundaries, etc.)and provides information to BMS controller 366 (e.g., temperaturemeasurements, valve or actuator positions, operating statuses,diagnostics, etc.). For example, AHU controller 330 may provide BMScontroller 366 with temperature measurements from temperature sensors362-364, equipment on/off states, equipment operating capacities, and/orany other information that can be used by BMS controller 366 to monitoror control a variable state or condition within building zone 306.

Client device 368 can include one or more human-machine interfaces orclient interfaces (e.g., graphical user interfaces, reportinginterfaces, text-based computer interfaces, client-facing web services,web servers that provide pages to web clients, etc.) for controlling,viewing, or otherwise interacting with HVAC system 100, its subsystems,and/or devices. Client device 368 can be a computer workstation, aclient terminal, a remote or local interface, or any other type of userinterface device. Client device 368 can be a stationary terminal or amobile device. For example, client device 368 can be a desktop computer,a computer server with a user interface, a laptop computer, a tablet, asmartphone, a PDA, or any other type of mobile or non-mobile device.Client device 368 may communicate with BMS controller 366 and/or AHUcontroller 330 via communications link 372.

Building Management Systems

Referring now to FIG. 4, a block diagram of a building management system(BMS) 400 is shown, according to some embodiments. BMS 400 can beimplemented in building 10 to automatically monitor and control variousbuilding functions. BMS 400 is shown to include BMS controller 366 and aplurality of building subsystems 428. Building subsystems 428 are shownto include a building electrical subsystem 434, an informationcommunication technology (ICT) subsystem 436, a security subsystem 438,a HVAC subsystem 440, a lighting subsystem 442, a lift/escalatorssubsystem 432, and a fire safety subsystem 430. In various embodiments,building subsystems 428 can include fewer, additional, or alternativesubsystems. For example, building subsystems 428 may also oralternatively include a refrigeration subsystem, an advertising orsignage subsystem, a cooking subsystem, a vending subsystem, a printeror copy service subsystem, or any other type of building subsystem thatuses controllable equipment and/or sensors to monitor or controlbuilding 10. In some embodiments, building subsystems 428 includewaterside system 200 and/or airside system 300, as described withreference to FIGS. 2-3.

Each of building subsystems 428 can include any number of devices,controllers, and connections for completing its individual functions andcontrol activities. HVAC subsystem 440 can include many of the samecomponents as HVAC system 100, as described with reference to FIGS. 1-3.For example, HVAC subsystem 440 can include a chiller, a boiler, anynumber of air handling units, economizers, field controllers,supervisory controllers, actuators, temperature sensors, and otherdevices for controlling the temperature, humidity, airflow, or othervariable conditions within building 10. Lighting subsystem 442 caninclude any number of light fixtures, ballasts, lighting sensors,dimmers, or other devices configured to controllably adjust the amountof light provided to a building space. Security subsystem 438 caninclude occupancy sensors, video surveillance cameras, digital videorecorders, video processing servers, intrusion detection devices, accesscontrol devices and servers, or other security-related devices.

Still referring to FIG. 4, BMS controller 366 is shown to include acommunications interface 407 and a BMS interface 409. Interface 407 mayfacilitate communications between BMS controller 366 and externalapplications (e.g., monitoring and reporting applications 422,enterprise control applications 426, remote systems and applications444, applications residing on client devices 448, etc.) for allowinguser control, monitoring, and adjustment to BMS controller 366 and/orsubsystems 428. Interface 407 may also facilitate communications betweenBMS controller 366 and client devices 448. BMS interface 409 mayfacilitate communications between BMS controller 366 and buildingsubsystems 428 (e.g., HVAC, lighting security, lifts, powerdistribution, business, etc.).

Interfaces 407, 409 can be or include wired or wireless communicationsinterfaces (e.g., jacks, antennas, transmitters, receivers,transceivers, wire terminals, etc.) for conducting data communicationswith building subsystems 428 or other external systems or devices. Invarious embodiments, communications via interfaces 407, 409 can bedirect (e.g., local wired or wireless communications) or via acommunications network 446 (e.g., a WAN, the Internet, a cellularnetwork, etc.). For example, interfaces 407, 409 can include an Ethernetcard and port for sending and receiving data via an Ethernet-basedcommunications link or network. In another example, interfaces 407, 409can include a Wi-Fi transceiver for communicating via a wirelesscommunications network. In another example, one or both of interfaces407, 409 can include cellular or mobile phone communicationstransceivers. In one embodiment, communications interface 407 is a powerline communications interface and BMS interface 409 is an Ethernetinterface. In other embodiments, both communications interface 407 andBMS interface 409 are Ethernet interfaces or are the same Ethernetinterface.

Still referring to FIG. 4, BMS controller 366 is shown to include aprocessing circuit 404 including a processor 406 and memory 408.Processing circuit 404 can be communicably connected to BMS interface409 and/or communications interface 407 such that processing circuit 404and the various components thereof can send and receive data viainterfaces 407, 409. Processor 406 can be implemented as a generalpurpose processor, an application specific integrated circuit (ASIC),one or more field programmable gate arrays (FPGAs), a group ofprocessing components, or other suitable electronic processingcomponents.

Memory 408 (e.g., memory, memory unit, storage device, etc.) can includeone or more devices (e.g., RAM, ROM, Flash memory, hard disk storage,etc.) for storing data and/or computer code for completing orfacilitating the various processes, layers and modules described in thepresent application. Memory 408 can be or include volatile memory ornon-volatile memory. Memory 408 can include database components, objectcode components, script components, or any other type of informationstructure for supporting the various activities and informationstructures described in the present application. According to someembodiments, memory 408 is communicably connected to processor 406 viaprocessing circuit 404 and includes computer code for executing (e.g.,by processing circuit 404 and/or processor 406) one or more processesdescribed herein.

In some embodiments, BMS controller 366 is implemented within a singlecomputer (e.g., one server, one housing, etc.). In various otherembodiments BMS controller 366 can be distributed across multipleservers or computers (e.g., that can exist in distributed locations).Further, while FIG. 4 shows applications 422 and 426 as existing outsideof BMS controller 366, in some embodiments, applications 422 and 426 canbe hosted within BMS controller 366 (e.g., within memory 408).

Still referring to FIG. 4, memory 408 is shown to include an enterpriseintegration layer 410, an automated measurement and validation (AM&V)layer 412, a demand response (DR) layer 414, a fault detection anddiagnostics (FDD) layer 416, an integrated control layer 418, and abuilding subsystem integration later 420. Layers 410-420 can beconfigured to receive inputs from building subsystems 428 and other datasources, determine optimal control actions for building subsystems 428based on the inputs, generate control signals based on the optimalcontrol actions, and provide the generated control signals to buildingsubsystems 428. The following paragraphs describe some of the generalfunctions performed by each of layers 410-420 in BMS 400.

Enterprise integration layer 410 can be configured to serve clients orlocal applications with information and services to support a variety ofenterprise-level applications. For example, enterprise controlapplications 426 can be configured to provide subsystem-spanning controlto a graphical user interface (GUI) or to any number of enterprise-levelbusiness applications (e.g., accounting systems, user identificationsystems, etc.). Enterprise control applications 426 may also oralternatively be configured to provide configuration GUIs forconfiguring BMS controller 366. In yet other embodiments, enterprisecontrol applications 426 can work with layers 410-420 to optimizebuilding performance (e.g., efficiency, energy use, comfort, or safety)based on inputs received at interface 407 and/or BMS interface 409.

Building subsystem integration layer 420 can be configured to managecommunications between BMS controller 366 and building subsystems 428.For example, building subsystem integration layer 420 may receive sensordata and input signals from building subsystems 428 and provide outputdata and control signals to building subsystems 428. Building subsystemintegration layer 420 may also be configured to manage communicationsbetween building subsystems 428. Building subsystem integration layer420 translate communications (e.g., sensor data, input signals, outputsignals, etc.) across a plurality of multi-vendor/multi-protocolsystems.

Demand response layer 414 can be configured to optimize resource usage(e.g., electricity use, natural gas use, water use, etc.) and/or themonetary cost of such resource usage in response to satisfy the demandof building 10. The optimization can be based on time-of-use prices,curtailment signals, energy availability, or other data received fromutility providers, distributed energy generation systems 424, fromenergy storage 427 (e.g., hot TES 242, cold TES 244, etc.), or fromother sources. Demand response layer 414 may receive inputs from otherlayers of BMS controller 366 (e.g., building subsystem integration layer420, integrated control layer 418, etc.). The inputs received from otherlayers can include environmental or sensor inputs such as temperature,carbon dioxide levels, relative humidity levels, air quality sensoroutputs, occupancy sensor outputs, room schedules, and the like. Theinputs may also include inputs such as electrical use (e.g., expressedin kWh), thermal load measurements, pricing information, projectedpricing, smoothed pricing, curtailment signals from utilities, and thelike.

According to some embodiments, demand response layer 414 includescontrol logic for responding to the data and signals it receives. Theseresponses can include communicating with the control algorithms inintegrated control layer 418, changing control strategies, changingsetpoints, or activating/deactivating building equipment or subsystemsin a controlled manner. Demand response layer 414 may also includecontrol logic configured to determine when to utilize stored energy. Forexample, demand response layer 414 may determine to begin using energyfrom energy storage 427 just prior to the beginning of a peak use hour.

In some embodiments, demand response layer 414 includes a control moduleconfigured to actively initiate control actions (e.g., automaticallychanging setpoints) which minimize energy costs based on one or moreinputs representative of or based on demand (e.g., price, a curtailmentsignal, a demand level, etc.). In some embodiments, demand responselayer 414 uses equipment models to determine an optimal set of controlactions. The equipment models can include, for example, thermodynamicmodels describing the inputs, outputs, and/or functions performed byvarious sets of building equipment. Equipment models may representcollections of building equipment (e.g., subplants, chiller arrays,etc.) or individual devices (e.g., individual chillers, heaters, pumps,etc.).

Demand response layer 414 may further include or draw upon one or moredemand response policy definitions (e.g., databases, XML files, etc.).The policy definitions can be edited or adjusted by a user (e.g., via agraphical user interface) so that the control actions initiated inresponse to demand inputs can be tailored for the user's application,desired comfort level, particular building equipment, or based on otherconcerns. For example, the demand response policy definitions canspecify which equipment can be turned on or off in response toparticular demand inputs, how long a system or piece of equipment shouldbe turned off, what setpoints can be changed, what the allowable setpoint adjustment range is, how long to hold a high demand setpointbefore returning to a normally scheduled setpoint, how close to approachcapacity limits, which equipment modes to utilize, the energy transferrates (e.g., the maximum rate, an alarm rate, other rate boundaryinformation, etc.) into and out of energy storage devices (e.g., thermalstorage tanks, battery banks, etc.), and when to dispatch on-sitegeneration of energy (e.g., via fuel cells, a motor generator set,etc.).

Integrated control layer 418 can be configured to use the data input oroutput of building subsystem integration layer 420 and/or demandresponse later 414 to make control decisions. Due to the subsystemintegration provided by building subsystem integration layer 420,integrated control layer 418 can integrate control activities of thesubsystems 428 such that the subsystems 428 behave as a singleintegrated supersystem. In some embodiments, integrated control layer418 includes control logic that uses inputs and outputs from a pluralityof building subsystems to provide greater comfort and energy savingsrelative to the comfort and energy savings that separate subsystemscould provide alone. For example, integrated control layer 418 can beconfigured to use an input from a first subsystem to make anenergy-saving control decision for a second subsystem. Results of thesedecisions can be communicated back to building subsystem integrationlayer 420.

Integrated control layer 418 is shown to be logically below demandresponse layer 414. Integrated control layer 418 can be configured toenhance the effectiveness of demand response layer 414 by enablingbuilding subsystems 428 and their respective control loops to becontrolled in coordination with demand response layer 414. Thisconfiguration may advantageously reduce disruptive demand responsebehavior relative to conventional systems. For example, integratedcontrol layer 418 can be configured to assure that a demandresponse-driven upward adjustment to the setpoint for chilled watertemperature (or another component that directly or indirectly affectstemperature) does not result in an increase in fan energy (or otherenergy used to cool a space) that would result in greater total buildingenergy use than was saved at the chiller.

Integrated control layer 418 can be configured to provide feedback todemand response layer 414 so that demand response layer 414 checks thatconstraints (e.g., temperature, lighting levels, etc.) are properlymaintained even while demanded load shedding is in progress. Theconstraints may also include setpoint or sensed boundaries relating tosafety, equipment operating limits and performance, comfort, fire codes,electrical codes, energy codes, and the like. Integrated control layer418 is also logically below fault detection and diagnostics layer 416and automated measurement and validation layer 412. Integrated controllayer 418 can be configured to provide calculated inputs (e.g.,aggregations) to these higher levels based on outputs from more than onebuilding subsystem.

Automated measurement and validation (AM&V) layer 412 can be configuredto verify that control strategies commanded by integrated control layer418 or demand response layer 414 are working properly (e.g., using dataaggregated by AM&V layer 412, integrated control layer 418, buildingsubsystem integration layer 420, FDD layer 416, or otherwise). Thecalculations made by AM&V layer 412 can be based on building systemenergy models and/or equipment models for individual BMS devices orsubsystems. For example, AM&V layer 412 may compare a model-predictedoutput with an actual output from building subsystems 428 to determinean accuracy of the model.

Fault detection and diagnostics (FDD) layer 416 can be configured toprovide on-going fault detection for building subsystems 428, buildingsubsystem devices (i.e., building equipment), and control algorithmsused by demand response layer 414 and integrated control layer 418. FDDlayer 416 may receive data inputs from integrated control layer 418,directly from one or more building subsystems or devices, or fromanother data source. FDD layer 416 may automatically diagnose andrespond to detected faults. The responses to detected or diagnosedfaults can include providing an alert message to a user, a maintenancescheduling system, or a control algorithm configured to attempt torepair the fault or to work-around the fault.

FDD layer 416 can be configured to output a specific identification ofthe faulty component or cause of the fault (e.g., loose damper linkage)using detailed subsystem inputs available at building subsystemintegration layer 420. In other exemplary embodiments, FDD layer 416 isconfigured to provide “fault” events to integrated control layer 418which executes control strategies and policies in response to thereceived fault events. According to some embodiments, FDD layer 416 (ora policy executed by an integrated control engine or business rulesengine) may shut-down systems or direct control activities around faultydevices or systems to reduce energy waste, extend equipment life, orassure proper control response.

FDD layer 416 can be configured to store or access a variety ofdifferent system data stores (or data points for live data). FDD layer416 may use some content of the data stores to identify faults at theequipment level (e.g., specific chiller, specific AHU, specific terminalunit, etc.) and other content to identify faults at component orsubsystem levels. For example, building subsystems 428 may generatetemporal (i.e., time-series) data indicating the performance of BMS 400and the various components thereof. The data generated by buildingsubsystems 428 can include measured or calculated values that exhibitstatistical characteristics and provide information about how thecorresponding system or process (e.g., a temperature control process, aflow control process, etc.) is performing in terms of error from itssetpoint. These processes can be examined by FDD layer 416 to exposewhen the system begins to degrade in performance and alert a user torepair the fault before it becomes more severe.

Referring now to FIG. 5, a block diagram of another building managementsystem (BMS) 500 is shown, according to some embodiments. BMS 500 can beused to monitor and control the devices of HVAC system 100, watersidesystem 200, airside system 300, building subsystems 428, as well asother types of BMS devices (e.g., lighting equipment, securityequipment, etc.) and/or HVAC equipment.

BMS 500 provides a system architecture that facilitates automaticequipment discovery and equipment model distribution. Equipmentdiscovery can occur on multiple levels of BMS 500 across multipledifferent communications busses (e.g., a system bus 554, zone buses556-560 and 564, sensor/actuator bus 566, etc.) and across multipledifferent communications protocols. In some embodiments, equipmentdiscovery is accomplished using active node tables, which provide statusinformation for devices connected to each communications bus. Forexample, each communications bus can be monitored for new devices bymonitoring the corresponding active node table for new nodes. When a newdevice is detected, BMS 500 can begin interacting with the new device(e.g., sending control signals, using data from the device) without userinteraction.

Some devices in BMS 500 present themselves to the network usingequipment models. An equipment model defines equipment objectattributes, view definitions, schedules, trends, and the associatedBACnet value objects (e.g., analog value, binary value, multistatevalue, etc.) that are used for integration with other systems. Somedevices in BMS 500 store their own equipment models. Other devices inBMS 500 have equipment models stored externally (e.g., within otherdevices). For example, a zone coordinator 508 can store the equipmentmodel for a bypass damper 528. In some embodiments, zone coordinator 508automatically creates the equipment model for bypass damper 528 or otherdevices on zone bus 558. Other zone coordinators can also createequipment models for devices connected to their zone busses. Theequipment model for a device can be created automatically based on thetypes of data points exposed by the device on the zone bus, device type,and/or other device attributes. Several examples of automatic equipmentdiscovery and equipment model distribution are discussed in greaterdetail below.

Still referring to FIG. 5, BMS 500 is shown to include a system manager502; several zone coordinators 506, 508, 510 and 518; and several zonecontrollers 524, 530, 532, 536, 548, and 550. System manager 502 canmonitor data points in BMS 500 and report monitored variables to variousmonitoring and/or control applications. System manager 502 cancommunicate with client devices 504 (e.g., user devices, desktopcomputers, laptop computers, mobile devices, etc.) via a datacommunications link 574 (e.g., BACnet IP, Ethernet, wired or wirelesscommunications, etc.). System manager 502 can provide a user interfaceto client devices 504 via data communications link 574. The userinterface may allow users to monitor and/or control BMS 500 via clientdevices 504.

In some embodiments, system manager 502 is connected with zonecoordinators 506-510 and 518 via a system bus 554. System manager 502can be configured to communicate with zone coordinators 506-510 and 518via system bus 554 using a master-slave token passing (MSTP) protocol orany other communications protocol. System bus 554 can also connectsystem manager 502 with other devices such as a constant volume (CV)rooftop unit (RTU) 512, an input/output module (IOM) 514, a thermostatcontroller 516 (e.g., a TEC5000 series thermostat controller), and anetwork automation engine (NAE) or third-party controller 520. RTU 512can be configured to communicate directly with system manager 502 andcan be connected directly to system bus 554. Other RTUs can communicatewith system manager 502 via an intermediate device. For example, a wiredinput 562 can connect a third-party RTU 542 to thermostat controller516, which connects to system bus 554.

System manager 502 can provide a user interface for any devicecontaining an equipment model. Devices such as zone coordinators 506-510and 518 and thermostat controller 516 can provide their equipment modelsto system manager 502 via system bus 554. In some embodiments, systemmanager 502 automatically creates equipment models for connected devicesthat do not contain an equipment model (e.g., IOM 514, third partycontroller 520, etc.). For example, system manager 502 can create anequipment model for any device that responds to a device tree request.The equipment models created by system manager 502 can be stored withinsystem manager 502. System manager 502 can then provide a user interfacefor devices that do not contain their own equipment models using theequipment models created by system manager 502. In some embodiments,system manager 502 stores a view definition for each type of equipmentconnected via system bus 554 and uses the stored view definition togenerate a user interface for the equipment.

Each zone coordinator 506-510 and 518 can be connected with one or moreof zone controllers 524, 530-532, 536, and 548-550 via zone buses 556,558, 560, and 564. Zone coordinators 506-510 and 518 can communicatewith zone controllers 524, 530-532, 536, and 548-550 via zone busses556-560 and 564 using a MSTP protocol or any other communicationsprotocol. Zone busses 556-560 and 564 can also connect zone coordinators506-510 and 518 with other types of devices such as variable air volume(VAV) RTUs 522 and 540, changeover bypass (COBP) RTUs 526 and 552,bypass dampers 528 and 546, and PEAK controllers 534 and 544.

Zone coordinators 506-510 and 518 can be configured to monitor andcommand various zoning systems. In some embodiments, each zonecoordinator 506-510 and 518 monitors and commands a separate zoningsystem and is connected to the zoning system via a separate zone bus.For example, zone coordinator 506 can be connected to VAV RTU 522 andzone controller 524 via zone bus 556. Zone coordinator 508 can beconnected to COBP RTU 526, bypass damper 528, COBP zone controller 530,and VAV zone controller 532 via zone bus 558. Zone coordinator 510 canbe connected to PEAK controller 534 and VAV zone controller 536 via zonebus 560. Zone coordinator 518 can be connected to PEAK controller 544,bypass damper 546, COBP zone controller 548, and VAV zone controller 550via zone bus 564.

A single model of zone coordinator 506-510 and 518 can be configured tohandle multiple different types of zoning systems (e.g., a VAV zoningsystem, a COBP zoning system, etc.). Each zoning system can include aRTU, one or more zone controllers, and/or a bypass damper. For example,zone coordinators 506 and 510 are shown as Verasys VAV engines (VVEs)connected to VAV RTUs 522 and 540, respectively. Zone coordinator 506 isconnected directly to VAV RTU 522 via zone bus 556, whereas zonecoordinator 510 is connected to a third-party VAV RTU 540 via a wiredinput 568 provided to PEAK controller 534. Zone coordinators 508 and 518are shown as Verasys COBP engines (VCEs) connected to COBP RTUs 526 and552, respectively. Zone coordinator 508 is connected directly to COBPRTU 526 via zone bus 558, whereas zone coordinator 518 is connected to athird-party COBP RTU 552 via a wired input 570 provided to PEAKcontroller 544.

Zone controllers 524, 530-532, 536, and 548-550 can communicate withindividual BMS devices (e.g., sensors, actuators, etc.) viasensor/actuator (SA) busses. For example, VAV zone controller 536 isshown connected to networked sensors 538 via SA bus 566. Zone controller536 can communicate with networked sensors 538 using a MSTP protocol orany other communications protocol. Although only one SA bus 566 is shownin FIG. 5, it should be understood that each zone controller 524,530-532, 536, and 548-550 can be connected to a different SA bus. EachSA bus can connect a zone controller with various sensors (e.g.,temperature sensors, humidity sensors, pressure sensors, light sensors,occupancy sensors, etc.), actuators (e.g., damper actuators, valveactuators, etc.) and/or other types of controllable equipment (e.g.,chillers, heaters, fans, pumps, etc.).

Each zone controller 524, 530-532, 536, and 548-550 can be configured tomonitor and control a different building zone. Zone controllers 524,530-532, 536, and 548-550 can use the inputs and outputs provided viatheir SA busses to monitor and control various building zones. Forexample, a zone controller 536 can use a temperature input received fromnetworked sensors 538 via SA bus 566 (e.g., a measured temperature of abuilding zone) as feedback in a temperature control algorithm. Zonecontrollers 524, 530-532, 536, and 548-550 can use various types ofcontrol algorithms (e.g., state-based algorithms, extremum seekingcontrol (ESC) algorithms, proportional-integral (PI) control algorithms,proportional-integral-derivative (PID) control algorithms, modelpredictive control (MPC) algorithms, feedback control algorithms, etc.)to control a variable state or condition (e.g., temperature, humidity,airflow, lighting, etc.) in or around building 10.

HVAC System with Automated Device Pairing and Dimensional MismatchHandling

Referring now to FIG. 6A, a block diagram of a HVAC system 600 is shown,according to an exemplary embodiment. HVAC system 600 is shown toinclude a controller 602, an input module 630, an output module 632,several sensors 640, and several actuation devices 650. In briefoverview, controller 602 receives sensor readings from sensors 640 viainput module 630 and uses the sensor readings to generate actuationsignals (e.g., control signals, setpoints, operating commands, etc.) foractuation devices 650. Controller 602 provides the actuation signals toactuation devices 650 via output module 632. Actuation devices 650operate to affect an environmental condition in a building (e.g.,temperature, humidity, airflow, etc.), which can be measured by sensors640 and provided as a feedback to controller 602.

Controller 602 can be any type of controller in a HVAC system or BMS. Insome embodiments, controller 602 is a zone controller configured tomonitor and control a building zone. For example, controller 602 can bea zone temperature controller, a zone humidity controller, a zonelighting controller, a VAV zone controller (e.g., VAV zone controllers524, 532, 536, 550), a COBP zone controller (e.g., COPB controller 530,548), or any other type of controller for a building zone. In otherembodiments, controller 602 is a system controller or subsystemcontroller. For example, controller 602 can be a BMS controller (e.g.,BMS controller 366), a central plant controller, a subplant controller,a supervisory controller for a HVAC system or any other type of buildingsubsystem (e.g., a controller for any of building subsystems 428). Insome embodiments, controller 602 is a field controller or devicecontroller configured to monitor and control the performance of a set ofHVAC devices or other building equipment. For example, controller 602can be an AHU controller (e.g., AHU controller 330), a thermostatcontroller (e.g., thermostat controller 516), a rooftop unit controller,a chiller controller, a damper controller, or any other type ofcontroller in a HVAC system or BMS.

Sensors 640 can include any of a variety of physical sensors configuredto measure a variable state or condition in a building. For example,sensors 640 are shown to include temperature sensors 641, humiditysensors 642, airflow sensors 643, lighting sensors 644, pressure sensors645, and voltage sensors 646. Sensors 640 can be distributed throughouta building and configured to measure various environmental conditions atdifferent locations in the building. For example, one of temperaturesensors 641 can be located in a first zone of the building andconfigured to measure the temperature of the first zone, whereas anotherof temperature sensors 641 can be located in a second zone of thebuilding and configured to measure the temperature of the second zone.Similarly, sensors 640 can be distributed throughout a HVAC system andconfigured to measure conditions at different locations in the HVACsystem. For example, one of temperature sensors 641 can be a supply airtemperature sensor configured to measure the temperature of the airflowprovided to a building zone from an AHU, whereas another of temperaturesensors 641 can be a return air temperature sensor configured to measurethe temperature of the airflow returning from the building zone to theAHU.

Sensors 640 are shown providing sensor readings to controller 602 viainput module 630. The sensor readings can include analog inputs, digitalinputs, measurements, data samples, and/or other types of data generatedby sensors 640. In some embodiments, sensors 640 provide analog inputsto input module 630 and input module 630 converts the analog inputs todigital data samples. Each data sample can include a data point andassociated metadata. The data point can include a measured valueattribute indicating the value of the measured variable and a timeattribute indicating the time at which the measured value was observed.The metadata can include a unit of measure (e.g., degrees C., degreesF., kPa, volts, Watts, m/s, etc.), a sampling rate, a sourcedescription, a location, a purpose, or other attributes describing theassociated data point. Controller 602 can receive the sensor readingsfrom input module 630 and store the sensor readings as time series datain a time series database 616 (described in greater detail below).Controller 602 can use the sensor readings and/or time series data togenerate appropriate actuation signals for actuation devices 650.

Actuation devices 650 can include any of a variety of physical devicesconfigured to affect a variable state or condition in a building. Forexample, actuation devices 650 are shown to include chillers 651,heaters 652, valves 653, air handling units (AHUs) 654, dampers 655, andactuators 656. Although only a few types of actuation devices 650 areshown, it should be understood that actuation devices 650 can includeany type of equipment or device configured to affect buildingconditions. For example, actuation devices 650 can power relays,switches, lights, pumps, fans, cooling towers, or other types ofbuilding equipment or central plant equipment. Actuation devices 650 caninclude some or all of the equipment in building 10, HVAC system 100,waterside system 200, airside system 300, BMS 400, and/or BMS 500, asdescribed with reference to FIGS. 1-5. Actuation devices 650 can operateto affect various building conditions including temperature, humidity,airflow, lighting, air quality, power consumption, or any other variablestate or condition in a building.

Actuation devices 650 are shown receiving actuation signals fromcontroller 602 via output module 632. In some embodiments, the actuationsignals are control signals for actuation devices 650 (e.g., operatingsetpoints, on/off commands, etc.). For example, the actuation signalscan include commands to activate or deactivate individual chillers 651or heaters 652 and/or commands to operate chillers 651 or heaters 652 ata variable capacity (e.g., operate at 20% capacity, 40% capacity, etc.).The actuation signals can include position setpoints for valves 653,dampers 655, or actuators 656. The position setpoints can includecommands to move to a fully closed position, a 50% open position, afully open position, or any intermediate position.

In some embodiments, the actuation signals are provided directly toactuation devices 650 from controller 602 and used to adjust a physicaloperation of actuation devices 650 (e.g., if controller 602 directlycontrols actuation devices 650). In other embodiments, the actuationsignals are provided to an intermediate controller for actuation devices650. For example, controller 602 can provide a setpoint to a localcontroller for one or more of actuation devices 650. The localcontroller can then generate control signals for actuation devices 650to achieve the setpoint received from controller 602.

Controller 602 can use the sensor readings from sensors 640 as feedbackto determine appropriate actuation signals for actuation devices 650.Controller 602 can be configured to use one or more feedback controlalgorithms (e.g., state-based algorithms, extremum seeking control (ESC)algorithms, proportional-integral (PI) control algorithms,proportional-integral-derivative (PID) control algorithms, modelpredictive control (MPC) algorithms, etc.) to control actuation devices650 based on the sensor readings. For example, if the sensor readingfrom one of temperature sensors 641 indicates that the temperature of aparticular building zone is below a temperature setpoint for thebuilding zone, controller 602 can provide an actuation signal to one ofheaters 652, dampers 655, or AHUs 654 to increase the amount of heatingprovided to the building zone.

In some embodiments, the feedback control actions performed bycontroller 602 require knowledge of the relationships between sensors640 and actuation devices 650. For example, in order to drive thetemperature measured by a particular temperature measured toward asetpoint, controller 602 may need to identify which of actuation devices650 is configured to affect the measured temperature. In other words,controller 602 may need to identify causal relationships between varioussensors 640 and actuation devices 650. If such relationships are notalready known, controller 602 can perform an automated device pairingprocess to establish associations between various sensors 640 andactuation devices 650.

Some buildings have hundreds of sensors 640 and actuation devices 650.It can be cumbersome, error prone, and time consuming to manuallyidentify the associations between sensors 640 and their correspondingactuation devices 650. Additionally, building evolve over time which canchange existing relationships between sensors 640 and actuation devices650. For example, remodeling a building can break existing relationshipsbetween paired sensors 640 and actuation devices 650 or create newrelationships that did not previously exist. Advantageously, theautomated device pairing process performed by controller 602 canautomatically identify causal relationships between various sensors 640and actuation devices 650 (e.g., heater A affects temperature sensor B,damper C affects flow sensor D, etc.). Once the causal relationshipshave been identified, controller 602 can store associations betweenrelated sensors 640 and actuation devices 650 and use the storedassociations to perform control actions.

Still referring to FIG. 6A, controller 602 is shown to include acommunications interface 604 and a processing circuit 606.Communications interface 604 can include wired or wirelesscommunications interfaces (e.g., jacks, antennas, transmitters,receivers, transceivers, wire terminals, etc.) for conducting datacommunications with external systems or devices (e.g., input module 630,output module 632, sensors 640, actuation devices 650, etc.). Datacommunications via communications interface 604 can be direct (e.g.,local wired or wireless communications) or via a communications network(e.g., a LAN, a WAN, the Internet, a cellular network, etc.). Forexample, communications interface 604 can include an Ethernet card andport for sending and receiving data via an Ethernet-based communicationslink or network, a Wi-Fi transceiver for communicating via a wirelesscommunications network, and/or cellular or mobile phone communicationstransceivers for communicating via a cellular communications network.

Processing circuit 606 is shown to include a processor 608 and memory610. Processing circuit 606 can be communicably connected tocommunications interface 604 such that processing circuit 606 and thevarious components thereof can send and receive data via communicationsinterface 604. Processor 608 can be implemented as a general purposeprocessor, an application specific integrated circuit (ASIC), one ormore field programmable gate arrays (FPGAs), a group of processingcomponents, or other suitable electronic processing components.

Memory 610 (e.g., memory, memory unit, storage device, etc.) can includeone or more devices (e.g., RAM, ROM, Flash memory, hard disk storage,etc.) for storing data and/or computer code for completing orfacilitating the various processes, layers and modules described in thepresent application. Memory 610 can be or include volatile memory ornon-volatile memory. Memory 610 can include database components, objectcode components, script components, or any other type of informationstructure for supporting the various activities and informationstructures described in the present application. In some embodiments,memory 610 is communicably connected to processor 608 via processingcircuit 606 and includes computer code for executing (e.g., byprocessing circuit 606 and/or processor 608) one or more processesdescribed herein.

Still referring to FIG. 6A, controller 602 is shown to include virtualsensors 614 and virtual actuation devices 612. Virtual sensors 614 caninclude logical representations of one or more physical sensors 640. Forexample, virtual sensors 614 can include data objects (e.g., BACnetobjects, JSON objects, etc.), each of which corresponds to a particularphysical sensor 640 and functions as a logical representation of thecorresponding physical sensor 640 within the memory 610 of controller602. Virtual sensors 614 can include various attributes which describethe corresponding physical sensors 640 and include the sensor readingsfrom the corresponding physical sensors 640. An example of a virtualsensor 614 shown below:

{ “unique identifier” : “ucis-2327-2127-sded-iesa”, “signature” :“7b1c018bf975c88fbe9df6292bf370b1”, “BACnet object” : { “objectidentifier” : “analog input #1101”, “object name” : “507_SP2.RET_AIR”,“description” : “return air temp”, “device type” : “thermistor”, “objecttype” : “analog input”, “units” : “DEG_F”, “present value” : “68”“update interval” : “15 min”, “status flags” : [ “in alarm”, “fault”,“overridden”, “out of service” ], “event status” : “normal”,“reliability” : “no fault detected”, “out of service” : “false”, } }

Virtual sensors 614 can include a unique identifier attribute and asignature attribute. In some embodiments, the unique identifierattribute is a text string uniquely identifying a particular virtualsensor 614 (e.g., ucis-2327-2127-sded-iesa). The signature attribute canbe generated by controller 602 as a function of the BACnet objectattribute values. For example, controller 602 can generate the signatureattribute using the following function:

Signature=MD5(CONCAT(“analog input #1101”,“507_SP2.RET_AIR”,“return airtemp”,“thermistor”,“analog input”,“DEG_F”,“15 min”,“68”)

where the CONCAT ( ) function is a string concatenation function of theattribute values and the MD5 ( ) function is a hashing functionproducing a hash value (e.g., a 128 bit hash value). When any of theattribute values change, the signature attribute will also change.Accordingly, the signature attribute enables capturing of source datachanges, including changes in the present value of the sensor reading.

Virtual sensors 614 can include a BACnet object attribute with varioussub-attributes describing the corresponding physical sensors 640. Forexample, virtual sensors 614 can include an object identifier attributewhich identifies the type of input (e.g., analog input, digital input,enumerated value input, binary input, etc.), an object name attributewhich names the corresponding physical sensor (e.g., 507_SP2.RET_AIR), adescription attribute which provides a description of the measured value(e.g., return air temperature, supply air temperature, relativehumidity, etc.), and a device type attribute which indicates the type ofphysical sensor 640 represented by the virtual sensor 614 (e.g.,thermistor, thermocouple, limit switch, piezoelectric, etc.).

Virtual sensors 614 can include a present value attribute whichindicates the present value of the sensor reading (e.g., 68), a unitsattribute which indicates the unit of measure of the present valueattribute (e.g., degrees F., degrees C., kPa, volts, etc.), and anupdate interval attribute which indicates how often the present valueattribute is updated (e.g., 15 minutes, 1 minute, 1 second, etc.). Insome embodiments, virtual sensors 614 include status attributes (e.g.,status flags, event status, reliability, etc.) which indicate thecurrent status of the corresponding physical sensor 640. The attributesof virtual sensors 614 can be updated in real time (e.g., continuouslyor periodically as defined by the update interval) to reflect thecurrent sensor readings and/or the status of the corresponding physicalsensors 640. In some embodiments, virtual sensors 614 include softwareagents which monitor the sensor readings and other information (e.g.,metadata) received from sensors 640 and update the correspondingattribute values accordingly.

Similarly, virtual actuation devices 612 can include logicalrepresentations of one or more physical actuation devices 650. Forexample, virtual actuation devices 612 can include data objects (e.g.,BACnet objects, JSON objects, etc.), each of which corresponds to aparticular physical actuation device 650 and functions as a logicalrepresentation of the corresponding physical actuation device 650 withinthe memory 610 of controller 602. Virtual actuation devices 612 caninclude various attributes which describe the corresponding physicalactuation devices 650. In some embodiments, virtual actuation devices612 include some or all of the same attributes of virtual sensors 614,as previously described. In some embodiments, virtual actuation devices612 include a present value attribute which includes the most recentvalue of the actuation signal provided to the virtual actuation device.

Still referring to FIG. 6A, controller 602 is shown to include a timeseries database 616. Virtual sensors 614 are shown providing sensorreadings to time series database 616, whereas virtual actuation devices612 are shown providing actuation signals to time series database 616.The sensor readings provided by virtual sensors 614 can include a seriesof sensor readings collected by each of sensors 640 over time.Similarly, the actuation signals provided by virtual actuation devices612 can include a series of values of the actuation signals provided toeach of actuation devices 650 over time. Time series database 616 canstore the sensor readings and actuation signals as time series data foreach of sensors 640 and actuation devices 650. Each time seriescorresponds to one of sensors 640 or actuation devices 650 and includesa series of data values received from the corresponding sensor 640 orprovided to the corresponding actuation device 650.

In some embodiments, each time series is a partially ordered tuple of aparticular data point and associated metadata, as shown in the followingequation:

Timeseries=<Data Point,Metadata>

Each data point may include a series of data values (e.g., sensorreading values or actuation signal values) and corresponding times atwhich those values were measured or provided. An example of a Data Pointis shown in the following equation:

Data Point=<time,value>

where each of time and value can include a vector of time series values.Metadata can include various attributes of the corresponding data point,as shown in the following equation:

Metadata=<unit,sampling rate,source description,location,purpose, . . .>

where each item of Metadata represents one of the attributes (andcorresponding attribute values) of the virtual sensor 614 or virtualactuation device 612 from which the time series values of the associateddata point were received. Time series database 616 can store each timeseries for use by other components of controller 602.

In some embodiments, time series database 616 stores each time series ofactuation signal values as an actuation signal time series t(x) as shownin the following equation:

t(x)={t ₁ ,t ₂ ,t ₃ , . . . ,t _(N-1) ,t _(N)}

where each element t_(i) of the actuation signal time series t(x) is thevalue of the actuation signal at a particular time (i.e., a sample ofthe actuation signal) and N is the total number of elements in theactuation signal time series t(x). Similarly, time series database 616can store each time series of sensor reading values as a sensor responsetime series r(x) as shown in the following equation:

r(x)={r ₁ ,r ₂ ,r ₃ , . . . ,r _(M-1) ,r _(M)}

where each element r_(i) of the sensor response time series r(x) is thevalue of the sensor response signal at a particular time (i.e., a sampleof the sensor response signal) and M is the total number of elements inthe sensor response time series r(x).

Controller 602 can automatically identify causal relationships betweenvarious sensors 640 and actuation devices 650 based on the time seriesdata associated therewith. For example, for each actuation signal timeseries t(x), controller 602 can identify one or more of the sensorresponse time series r(x) which closely match the actuation signal timeseries. In some embodiments, controller 602 uses a distance function todetermine which of the sensor response time series r(x) most closelymatch the actuation signal time series t(x). As described in detailbelow, the distance function may compare corresponding values t_(i) andr_(i) of each time series to calculate a similarity metric or similarityscore for pairs of actuation signal time series t(x) and sensor responsetime series r(x).

In some embodiments, the similarity metric calculation performed bycontroller 602 requires the actuation signal time series t(x) and sensorresponse time series r(x) to have the same number of samples (i.e., N=M)and/or sampling rate to allow the corresponding values t_(i) and r_(i)of each time series to be identified and compared. However, some timeseries can have different numbers of samples (i.e., N≠M), which can becollected at different sampling rates. This is referred to as adimensional mismatch between time series. A dimensional mismatch betweentime series can complicate the similarity metric calculation since itcan be difficult to determine the corresponding values t_(i) and r_(i)of each time series. However, controller 602 can automatically identifyand compensate for dimensional mismatches between time series.

Still referring to FIG. 6A, controller 602 is shown to include adimensional mismatch identifier 618. Dimensional mismatch identifier 618is configured to identify dimensional mismatches between variousactuation signal time series t(x) and sensor response time series r(x).As described above, a dimensional mismatch may occur when two timeseries have a different number of samples and/or sampling rates. In someembodiments, dimensional mismatch identifier 618 determines the size ofeach time series. For example, dimensional mismatch identifier 618 candetermine the number of samples N in the actuation signal time seriest(x) and the number of samples M in the sensor response time seriesr(x). Dimensional mismatch identifier 618 can detect a dimensionalmismatch in response to a determination that the number of samples N inthe actuation signal time series t(x) is different from the number ofsamples M in the sensor response time series r(x) (i.e., N≠M).

In some embodiments, dimensional mismatch identifier 618 determines thesampling rate of each time series. In some embodiments, the samplingrate of a time series may be stored as metadata associated with the timeseries in time series database 616. Dimensional mismatch identifier 618can determine the sampling rate of a time series by reading the samplingrate from the metadata in time series database 616. In otherembodiments, dimensional mismatch identifier 618 calculates the samplingrate for one or more time series based on the size of the time seriesand the range of time spanned by the time series.

Dimensional mismatch identifier 618 can identify a start time and an endtime for the time series by reading the timestamps associated with thefirst and last data samples in the time series. Dimensional mismatchidentifier 618 can then calculate the sampling rate by dividing the sizeof the time series by the difference between the end time and the starttime, as shown in the following equation:

${sampling\_ rate} = \frac{{size\_ of}{\_ timeseries}}{{end\_ time} - {start\_ time}}$

where size_of_timeseries is the number of samples M or N in the timeseries, end_time is the timestamp associated with the last sample in thetime series, start_time is the timestamp associated with the firstsample in the time series, and sampling_rate is the sampling rate of thetime series, expressed as the number of samples per unit time (e.g., 0.8samples/hour). Dimensional mismatch identifier 618 can detect adimensional mismatch in response to a determination that two time serieshave different sampling rates.

Dimensional mismatch identifier 618 can be configured to correct adimensional mismatch between two time series. In some embodiments,dimensional mismatch identifier 618 corrects dimensional mismatch byincreasing the number of samples of the time series with the fewernumber of samples (e.g., by interpolating between samples). In otherembodiments, dimensional mismatch identifier 618 corrects dimensionalmismatch by reducing the number of samples of the time series with thegreater number of samples (e.g., by discarding extra samples). In otherembodiments, dimensional mismatch identifier 618 merely identifies adimensional mismatch to other components of controller 602 which areconfigured to address the dimensional mismatch. For example, dimensionalmismatch identifier 618 is shown reporting a dimensional mismatch todiscrete cosine transformer 620.

Still referring to FIG. 6A, controller 602 is shown to include adiscrete cosine transformer 620. Discrete cosine transformer 620 can beconfigured to perform a discrete cosine transform (DCT) for eachactuation signal time series t(x) and sensor response time series r(x).A DCT expresses a finite sequence of data points in terms of a sum ofcosine functions oscillating at different frequencies. In particular, aDCT is a Fourier-related transform similar to the discrete Fouriertransform (DFT), but using only real numbers. There are eight standardDCT variants, commonly referred to as DCT-I, DCT-II, DCT-III, DCT-IV,DCT-V, DCT-VI, DCT-VII, and DCT-VIII. One of these variants (i.e.,DCT-II) is discussed in detail below. However, it should be understoodthat discrete cosine transformer 620 can use any standard ornon-standard DCT variant in other embodiments.

In some embodiments, discrete cosine transformer 620 performs a DCT foreach actuation signal time series t(x) using the following equation:

${T(k)} = {\sum\limits_{i = 0}^{N - 1}{t_{i}{\cos \left\lbrack {\frac{\pi}{N}\left( {i + \frac{1}{2}} \right)k} \right\rbrack}}}$k = 0, …  , N − 1

where T(k) is the kth coefficient of the DCT of the actuation signaltime series t(x), t_(i) is the ith sample of the actuation signal timeseries t(x), and N is the number of samples of the actuation signal timeseries t(x). Discrete cosine transformer 620 can generate an array T ofthe DCT coefficients (e.g., T=[T(0), T(1), T(2), . . . , T(N−2),T(N−1)]) where the length of the array T is the same as the number ofsamples N of the actuation signal time series t(x).

Similarly, discrete cosine transformer 620 can perform a DCT for eachsensor response time series r(x) using the following equation:

${R(k)} = {\sum\limits_{i = 0}^{M - 1}{r_{i}{\cos \left\lbrack {\frac{\pi}{M}\left( {i + \frac{1}{2}} \right)k} \right\rbrack}}}$k = 0, …  , M − 1

where R(k) is the kth coefficient of the DCT of the sensor response timeseries r(x), r_(i) is the ith sample of the sensor response time seriesr(x), and M is the number of samples of the sensor response time seriesr(x). Discrete cosine transformer 620 can generate an array R of the DCTcoefficients (e.g., R=[R(0), R(1), R(2), . . . , R(M−2), R(M−1)]) wherethe length of the array R is the same as the number of samples M of thesensor response time series r(x).

The following example illustrates the result of applying DCT to an inputtime series X(n). The input time series X(n) can be an actuation signaltime series t(x) or a sensor response time series r(x) as previouslydescribed. The samples of the input time series X(n) are shown in thefollowing array:

X(n)=[1.00,1.70,2.00,2.00,4.30,4.50,3.00,3.00,2.30,2.20,2.20,2.30]

where the input time series X(n) includes twelve time series valuesX(1), . . . , X(12). Applying DCT to the input time series X(n) resultsin a set of DCT coefficients, shown in the following array:

Y(k)=[8.80,−0.57,−2.65,−1.15,0.81,0.52,−0.20,−0.93,−0.63,0.32,0.50,−0.06]

where the array of DCT coefficients Y(k) includes twelve DCTcoefficients Y(1), . . . , Y(12).

Still referring to FIG. 6A, controller 602 is shown to include a DCTquantizer 622. DCT quantizer 622 can be configured to apply aquantization process to the sets of DCT coefficients generated bydiscrete cosine transformer 620. As described above, the DCT processperformed by discrete cosine transformer 620 converts an input data timeseries X(n) into a sum of cosine functions which oscillate at differentfrequencies. The cosine function with the lowest frequency is typicallyfirst in the summation, followed by cosine functions with successivelyhigher frequencies. Accordingly, the DCT coefficient which occurs firstin the array of DCT coefficients Y(k) represents the magnitude of thelowest frequency cosine function. Each of the following DCT coefficientsrepresents the magnitude of a cosine function with a successively higheroscillation frequency.

DCT quantizer 622 can apply a quantization process to the sets of DCTcoefficients by filling some of the higher frequency DCT coefficientswith zeros. This has the effect of removing some of the higher frequencycomponents (i.e., cosine functions) from the summation while retainingthe lower frequency components. In some embodiments, DCT quantizer 622performs the quantization process using a predetermined quantizationlevel. The quantization level may define the number of the DCTcoefficients which are retained (i.e., not filled with zeros). Forexample, a quantization level of six may retain the DCT coefficientsapplied to the six lowest frequency cosine functions (e.g., the firstsix DCT coefficients in the array) while the remaining DCT coefficientsare filled with zeros.

The following example illustrates the result of a quantization processwhich can be performed by DCT quantizer 622. DCT quantizer 622 canmodify the array of DCT coefficients Y(k) shown above to form thefollowing quantized array QY(k):

QY(k)=[8.80,−0.57,−2.65,−1.15,0.81,0.52,0.00,0.00,0.00,0.00,0.00,0.00]

In this example, a quantization level of six is applied, meaning thatonly the first six DCT coefficients are retained from the original arrayof DCT coefficients Y(k). The remaining DCT coefficients are filled withzeros. True compression can be achieved by not storing the zeros. Forexample, DCT quantizer 622 can store the following compressed arrayC(k):

C(k)=[8.80,−0.57,−2.65,−1.15,0.81,0.52]

in which the coefficients filled with zeros are discarded to produce acompressed array with a length equal to the quantization level applied.In this example, the compressed array C(k) has a length of six resultingfrom the use of a quantization level of six. In various embodiments, DCTquantizer 622 can use a quantization level of six or any otherquantization level to produce compressed arrays of various lengths.

In some embodiments, DCT quantizer 622 automatically determines thequantization level to apply based on the number of samples in each ofthe original actuation signal time series t(x) and sensor response timeseries r(x). As described above, the number of DCT coefficients producedby discrete cosine transformer 620 for a given input time series X(n)may be equal to the number of samples in the time series X(n) prior toperforming DCT. For example, an input time series X₁(n) with twelvesamples may result in twelve DCT coefficients in the resultant DCTcoefficient array Y₁(k), whereas an input time series X₂(n) with tensamples may result in ten DCT coefficients in the resultant DCTcoefficient array Y₂(k). In some embodiments, DCT quantizer 622identifies the actuation signal time series t(x) or sensor response timeseries r(x) with the fewest samples and applies a quantization levelequal to the number of samples in the identified time series.

In some embodiments, DCT quantizer 622 applies the same quantizationlevel to the sets of DCT coefficients corresponding to each of theoriginal actuation signal time series t(x) and sensor response timeseries r(x). Using the same quantization level for each of the originaltime series may result in the same number of compressed DCT coefficientsbeing stored for each of the original actuation signal time series t(x)and sensor response time series r(x). In some embodiments, the number ofstored DCT coefficients is equal to the number of samples in theoriginal time series with the fewest samples. Advantageously, thisallows for direct comparison of the DCT coefficients in the compressedarrays C(k) generated for each of the original time series withoutrequiring decompression, interpolation, synchronization, or otherprocessing steps after the compressed arrays C(k) are generated.

In some embodiments, DCT quantizer 622 generates a compressed timeseries Ta based on each compressed array of DCT coefficients. DCTquantizer 622 can store the compressed time series T_(α) using thefollowing data structure:

T _(α)=

α,δ,ρ,κ,

ψ

,

υ₁,υ₂, . . . υ_(p)

where α is the time series ID of the source time series (e.g., theactuation signal time series t(x) or sensor response time series r(x)),δ is the dimension of the source time series (e.g., the number ofsamples in the source time series), ρ is the quantization level appliedby DCT quantizer 622, κ is a pointer for metadata,

ψ

indicates the start time and end time of samples in the source timeseries, and

υ₁, υ₂, . . . υ_(p)

is the array of compressed DCT coefficients. An example of a compressedtime series stored using this data structure is as follows:

T ₂₀₃=

203,12,6,

2016:10:05:12:00:00,2016:10:05:13:00:00

,

8.80,−0.57,−2.65,−1.15,0.81,0.52

where 203 is the time series ID of the source time series, 12 is size ofthe source time series (e.g., 12 samples in the source time series), 6is the quantization level applied by DCT quantizer 622,2016:10:05:12:00:00 is the start time of the source time series (e.g.,the timestamp of the earliest sample in the source time series),2016:10:05:13:00:00 is the end time of the source time series (e.g., thetimestamp of the latest sample in the source time series), and the array

8.80, −0.57, −2.65, −1.15, 0.81, 0.52

includes the compressed DCT coefficients generated by DCT quantizer 622.

Still referring to FIG. 6A, controller 602 is shown to include asimilarity calculator 624. Similarity calculator 624 can be configuredto determine whether two time series are similar to each other based onthe compressed DCT coefficients and/or compressed time series generatedby DCT quantizer 622. In some embodiments, similarity calculator 624determines whether any of the sensor response time series r(x) aresimilar to a given actuation signal time series t(x). Similaritycalculator 624 can repeat this process for each actuation signal timeseries t(x) to determine whether any of the sensor response time seriesr(x) are similar to each actuation signal time series t(x).

In some embodiments, similarity calculator 624 determines whether twotime series are similar to each other by calculating a similarity metricfor the two time series. The similarity metric can be based on thecompressed DCT coefficients generated by DCT quantizer 622 for the twotime series. For example, the compressed DCT coefficients generated fora given actuation signal time series t(x) can be represented by an arrayT, whereas the compressed DCT coefficients generated for a given sensorresponse time series r(x) can be represented by an array R. The arrays Tand R can be particular instances of the compressed array C(k) generatedby DCT quantizer 622 for the actuation signal time series t(x) and thesensor response time series r(x), respectively. Each array T and R caninclude a predetermined number N of DCT coefficients, defined by thequantization level applied by DCT quantizer 622. Examples of arrays Tand R are as follows:

T=

t ₁ ,t ₂ , . . . t _(N)

R=

r ₁ ,r ₂ , . . . ,r _(N)

Similarity calculator 624 can calculate a similarity metric for thesource time series t(x) and r(x) based on the corresponding arrays T andR of compressed DCT coefficients. In some embodiments, similaritycalculator 624 calculates the similarity metric using the followingequation:

${d\left( {T,R} \right)} = {\sum\limits_{i = 1}^{i = N}\frac{\sqrt{\left( {t_{i} - r_{i}} \right)^{2}}}{\delta_{i}}}$

where t_(i) is the ith DCT coefficient in the array T based on theactuation signal time series t(x), r_(i) is the ith DCT coefficient inthe array R based on the sensor response time series r(x), δ_(i) is thestandard deviation of the ith DCT coefficients, and N is the number ofDCT coefficients in each array T and R. Low values of the similaritymetric d(T, R) indicate a greater similarity, whereas high values of thesimilarity metric d (T, R) indicate a lesser similarity. Similaritycalculator 624 can calculate a similarity metric for each pairing of anactuation signal time series t(x) and a sensor response time seriesr(x).

Still referring to FIG. 6A, controller 602 is shown to include a devicepairing generator 626. Device pairing generator 626 is shown receivingthe similarity metrics from similarity calculator 624. Device pairinggenerator 626 can be configured to generate device pairings based on thesimilarity metrics. Each device pairing may include one of sensors 640and one of actuation devices 650. A device pairing may indicate that theactuation device 650 in the device pairing is configured to affect thevariable measured by the sensor 640 in the device pairing. For eachpotential device pairing (i.e., for each combination of a sensor 640 andan activation device 650), device pairing generator 626 can identify thecorresponding arrays T and R of compressed DCT coefficients and thecalculated similarity metric d(T, R) based on the arrays T and R. Devicepairing generator 626 can use the similarity metric d(T, R) to determinewhether to generate a device pairing between the given sensor 640 andthe given actuation device 650.

In some embodiments, device pairing generator 626 generates devicepairings by comparing the similarity metric d(T, R) to a thresholdvalue. Device pairing generator 626 can be configured to generate adevice pairing between a sensor 640 and an actuation device 650 if thesimilarity metric d(T, R) is less than the threshold value (e.g., d(T,R)<threshold). The threshold value can be a predefined value or acalculated value (e.g., a standard deviation of the DCT coefficients).

In some embodiments, the threshold value is a similarity metric betweenthe actuation signal time series t(x) and a baseline (e.g., average)sensor signal time series a(x) over a predetermined time period. Thebaseline sensor signal time series a(x) can indicate the average sensorresponse from a particular sensor 640 before the actuation signal isapplied to the actuation device 650 (e.g., baseline sensor readings),whereas the sensor response time series r(x) can indicate the sensorresponse from the same sensor 640 after the actuation signal is appliedto the actuation device 650. If the actuation device 650 affects thesensor 640, the actuation signal time series t(x) is expected to be moresimilar to the sensor response time series r(x) than the baseline sensorsignal time series a(x). Accordingly, the similarity metric d(T, R)between the actuation signal time series t(x) and the sensor responsetime series r(x) is expected to be lower (i.e., more similar) than thesimilarity metric d(T, A) between the actuation signal time series t(x)and the baseline sensor signal time series a(x).

In some embodiments, time series database 616 stores a baseline sensorsignal time series a(x) for each of sensors 640 based on sensor readingsfrom sensors 640 before the actuation signal is applied to actuationdevices 650. Time series database 616 can also store a sensor responsetime series r(x) for each of sensors 640 based on sensor readings fromsensors 640 while the actuation signal is applied to actuation devices650 or after the actuation signal is applied to actuation devices 650.Discrete cosine transformer 620 and DCT quantizer 622 can generate DCTcoefficients and compressed DCT coefficients for each baseline sensorsignal time series a(x), sensor response time series r(x), and actuationsignal time series t(x). Similarity calculator 624 can then calculate asimilarity metric d(T, R) between each actuation signal time series t(x)and sensor response time series r(x) and a similarity metric d(T, A)between each actuation signal time series t(x) and baseline sensorsignal time series a(x). Device pairing generator 626 can generate adevice pairing between a sensor 640 and an actuation device 650 if thesimilarity metric d (T, R) for a given combination of a sensor 640 andan actuation device 650 is less than the similarity metric d(T,A) forthe sensor 640 and the actuation device 650.

In some embodiments, device pairing generator 626 generates devicepairings by comparing the similarity metrics d(T, R) for variouscombinations of sensors 640 and actuation devices 650. For eachactuation device 650, device pairing generator 626 can identify thesimilarity metrics d(T, R) calculated for the actuation device 650 incombination with each of sensors 640. Each similarity metric d(T, R)indicates the similarity (i.e., the closeness) between the actuationsignal time series t(x) associated with the actuation device 650 and thesensor response time series r(x) associated with one of sensors 640. Forexample, the similarity metric d(T₁, R₁) may indicate the similaritybetween a first actuation device 650 (corresponding to array T₁) and afirst sensor 640 (corresponding to array R₁), whereas the similaritymetric d(T₁, R₂) may indicate the similarity between the first actuationdevice 650 and a second sensor 640 (corresponding to array R₂). Deviceparing generator 626 can identify all of the similarity metricsassociated with a given actuation device 650 (e.g., d(T₁, R₁), . . . ,d(T₁, R_(P)), where P is the total number of sensors 640 and/or sensorresponse time series r(x)).

Device pairing generator 626 can determine which of the identifiedsimilarity metrics is the lowest for a given actuation device 650. Thelowest similarity metric indicates the closest match between theactuation signal time series t(x) associated with the actuation device650 and the sensor response time series r(x) associated with one ofsensors 640. Device pairing generator 626 can generate a device pairingbetween the actuation device 650 and the sensor 640 having the lowestsimilarity metric d(T, R) with the actuation device 650. If theactuation device 650 has the same similarity metric with multiplesensors 640 (e.g., d(T₁, R₁)=d(T₁, R₂)), device pairing generator 626can examine the time delay Δw between the actuation signal time seriest(x) associated with the actuation device 650 and sensor response timeseries r(x) associated with each of sensors 640. The time delay Δw mayindicate the delay between the time w₁ at which the actuation signal isapplied to the actuation device 650 and the time w₂ at which the effectsof the actuation signal are evident in the sensor response (e.g.,Δw=w₂−w₁). Device pairing generator 626 can determine which of thesensors 640 has the lowest time delay Δw and can generate a devicepairing between the actuation device 650 and the sensor 640 with thelowest time delay Δw.

Device pairing generator 626 can generate one or more device pairingsfor each of actuation devices 650. Each device pairing can identify oneof actuation devices 650 and one of sensors 640. A device pairingbetween an actuation device 650 and a sensor 640 indicates that theactuation device 650 is capable of affecting the value measured by thesensor. Device pairing generator 626 can provide the device pairings todevice controller 628. Device controller 628 can use the device pairingsto generate the actuation signals for actuation devices 650. In someembodiments, device controller 628 uses the device pairings toautomatically generate and store causal relationships between varioussensors 640 and actuation devices 650.

In some embodiments, device controller 628 uses the device pairings tocreate kits of causally related devices. Each kit may be a logicalgrouping or set of devices in HVAC system 600 which includes one or moreof sensors 640 and one or more of actuation devices 650. In someembodiments, each kit includes all of the sensors 640 and actuationdevices 650 that are linked to each other by the device pairingsgenerated by device pairing generator 626. Each device in a given kitmay have a device pairing with at least one other device in the kit. Forexample, a temperature sensor for a building zone may have a devicepairing with a chiller which is operable to affect the temperature ofthe building zone. The temperature sensor may also have device pairingswith an air handling unit and an airflow damper which operate to provideairflow to the building zone. The kit generated by device controller 628may include the temperature sensor and all of the actuation deviceswhich operate to affect the temperature of the building zone (e.g., thechiller, the air handling unit, the airflow damper, etc.).

In some embodiments, device controller 628 uses the kits to detect anddiagnose faults or performance issues in the building. For example, atemperature fault for a building zone (e.g., temperature out of range)can be detected by a temperature sensor located in the building zone.However, the temperature fault may originate from a fault in one or moreof the actuation devices 650 which affect the temperature measured bythe temperature sensor. Device controller 628 can use the kits ofcausally related devices to identify one or more actuation devices 650(e.g., a chiller, a heater, an air handling unit, a damper, etc.) whichoperate to affect the variable in fault (e.g., the temperature measuredby the temperature sensor). Device controller 628 can then test theactuation devices 650 in the kit to determine whether any of theactuation devices 650 are operating abnormally and diagnose the cause ofthe fault.

In some embodiments, device controller 628 uses the kits to generaterecommendations for resolving the fault. For example, device controller628 may recommend that all of the actuation devices 650 in a kit betested or investigated in order to determine which of the actuationdevices 650 is contributing to a detected fault associated with a sensor640 in the kit. In some embodiments, the recommendation is based on aduration of the fault. For example, if the fault has been occurring foran amount of time which is less than a duration threshold, devicecontroller 628 may recommend that one or more of the devices in the kitbe investigated or tested. However, if the fault has been occurring foran amount of time which exceeds the duration threshold, devicecontroller 628 may recommend that one or more of the devices in the kitbe replaced, repaired, or changed in order to resolve the fault.

In some embodiments, device controller 628 provides actuation devices650 with test signals as part of the device pairing process. The testsignals may be predetermined signals or sequences of control operationswhich differ from the control signals provided to actuation devices 650during normal operation. In some embodiments, the test signals are theactuation signals t(x) used by other components of controller 602 togenerate the device pairings. Device controller 628 can provide the testsignals to actuation devices 650 via communications interface 604 and tovirtual actuation devices 612. Virtual actuation devices 612 can updatetheir status in real time based on the test signals.

In some embodiments, device controller 628 uses the device pairings tocreate feedback control loops for HVAC system 600. Each feedback controlloop can receive a feedback signal from one or more of sensors 640 andcan provide a control signal to one or more of actuation devices 650.Device controller 628 can use the device pairings to define the sensors640 and actuation devices 650 in each control loop. For example, devicecontroller 628 can create a control loop which receives a feedbacksignal from the sensor 640 in a device pairing and provides a controlsignal to the actuation device 650 in the device pairing. Devicecontroller 628 can map the sensor readings from the sensor 640 in thedevice pairing to the feedback signal in the control loop. Similarly,device controller 628 can map the actuation signals provided to theactuation device 650 in the device pairing to the control signal in thecontrol loop.

Device controller 628 can use the feedback control loops to generate theactuation signals for actuation devices 650. Device controller 628 canuse state-based algorithms, extremum seeking control (ESC) algorithms,proportional-integral (PI) control algorithms,proportional-integral-derivative (PID) control algorithms, modelpredictive control (MPC) algorithms, or any other type of controlmethodology to generate the actuation signals for actuation devices 650based on the sensor readings. For example, if the sensor reading fromone of temperature sensors 641 indicates that the temperature of aparticular building zone is below a temperature setpoint for thebuilding zone, device controller 628 can provide an actuation signal toone of heaters 652, dampers 655, or AHUs 654 to increase the amount ofheating provided to the building zone. Advantageously, the relationshipsbetween actuation devices 650 and sensors 640 can be identifiedautomatically based on the device pairings to allow device controller628 to determine which of actuation devices 650 can be operated toaffect a given sensor reading.

Referring now to FIG. 6B, a block diagram illustrating a portion of HVACsystem 600 in greater detail is shown, according to an exemplaryembodiment. HVAC system 600 is shown to include a smart actuator 658,sensors 640, and a valve/damper 672. Smart actuator 658 may be one ofactuation devices 650 or a separate actuator in HVAC system 600.Valve/damper 672 may be an airflow damper, a fluid control valve, anexpansion valve, or any other type of flow control device in HVAC system600. Smart actuator 658 can be configured to operate valve/damper 672(e.g., by opening and closing valve/damper 672) based on sensor readingsreceived from sensors 640. Advantageously, smart actuator 658 canautomatically determine which of sensors 640 is affected by valve/damper672 and can operate valve/damper 672 based on the sensor readings fromthe affected sensor or sensors 640.

Smart actuator 658 is shown to include an actuation device 674 having amotor 676 and a drive device 678. Drive device 678 may be mechanicallycoupled to valve/damper 672 and configured to open and closevalve/damper 672 when operated by motor 676. Motor 676 may bemechanically coupled to drive device 678 and configured to operate drivedevice 678 based on actuation signals received from processing circuit606. Unlike conventional actuators, smart actuator 658 can independentlyand automatically determine appropriate actuation signals for actuationdevice 674 without requiring input from an external controller.

Smart actuator 658 is shown to include a communications interface 680and a processing circuit 606. Communications interface 680 may be thesame or similar to communications interface 604, as described withreference to FIG. 6A. Communications interface 680 can include wired orwireless communications interfaces (e.g., jacks, antennas, transmitters,receivers, transceivers, wire terminals, etc.) for conducting datacommunications with external systems or devices (e.g., sensors 640, userdevices, supervisory controllers, etc.). Data communications viacommunications interface 680 can be direct (e.g., local wired orwireless communications) or via a communications network (e.g., a LAN, aWAN, the Internet, a cellular network, etc.). For example,communications interface 680 can include an Ethernet card and port forsending and receiving data via an Ethernet-based communications link ornetwork, a Wi-Fi transceiver for communicating via a wirelesscommunications network, and/or cellular or mobile phone communicationstransceivers for communicating via a cellular communications network.

Processing circuit 606 may include some or all of the components ofprocessing circuit 606 shown in FIG. 6A. For example, processing circuit606 is shown to include a processor 608 and memory 608. Processingcircuit 606 can be communicably connected to communications interface680 such that processing circuit 606 and the various components thereofcan send and receive data via communications interface 680. Processor608 can be implemented as a general purpose processor, an applicationspecific integrated circuit (ASIC), one or more field programmable gatearrays (FPGAs), a group of processing components, or other suitableelectronic processing components.

Memory 610 (e.g., memory, memory unit, storage device, etc.) can includeone or more devices (e.g., RAM, ROM, Flash memory, hard disk storage,etc.) for storing data and/or computer code for completing orfacilitating the various processes, layers and modules described in thepresent application. Memory 610 can be or include volatile memory ornon-volatile memory. Memory 610 can include database components, objectcode components, script components, or any other type of informationstructure for supporting the various activities and informationstructures described in the present application. In some embodiments,memory 610 is communicably connected to processor 608 via processingcircuit 606 and includes computer code for executing (e.g., byprocessing circuit 606 and/or processor 608) one or more processesdescribed herein.

Memory 610 may include some or all of the components of memory 610 shownin FIG. 6A. For example, memory 610 may include virtual actuationdevices 612, virtual sensors 614, time series database 616, dimensionalmismatch identifier 618, discrete cosine transformer 620, DCT quantizer622, similarity calculator 624, device pairing generator 626, and devicecontroller 628. When implemented in smart actuator 658, device pairinggenerator 626 can generate pairings between actuation device 674 and oneor more of sensors 640 using the techniques described with reference toFIG. 6A. In other words, device pairing generator 626 can determinewhich of sensors 640 is/are affected by actuation device 674. Devicecontroller 628 can use the device pairings and the sensor readings fromthe affected sensors 640 to generate actuation signals for actuationdevice 674.

Referring now to FIG. 6C, a block diagram illustrating another portionof HVAC system 600 in greater detail is shown, according to an exemplaryembodiment. HVAC system 600 is shown to include a smart chiller 659,sensors 640, and a building zone 670. Smart chiller 659 may be one ofactuation devices 650 or a separate chiller in HVAC system 600. Smartchiller 659 can be configured to provide cooling for building zone 670based on sensor readings received from sensors 640. For example, smartchiller 659 is shown to include a refrigeration circuit 660 having acompressor 662, a condenser 664, an expansion device 666, and anevaporator 668. Compressor 662 can be configured to circulate arefrigerant between condenser 664 and evaporator 668 based on actuationsignals received from processing circuit 606. Evaporator 668 can providecooling for an airflow provided to building zone 670 either directly(e.g., by directly chilling the airflow) or via an intermediate coolant(e.g., by chilling a coolant which is used to chill the airflow). Unlikeconventional chillers, smart chiller 659 can independently andautomatically determine appropriate actuation signals for refrigerationcircuit 660 without requiring input from an external controller.

Smart chiller 659 can automatically determine which of sensors 640is/are affected by refrigeration circuit 660 and can operaterefrigeration circuit 660 based on the sensor readings from the affectedsensor(s) 640. For example, sensors 640 are shown to include a zone Atemperature sensor 641 a, a zone B temperature sensor 641 b, and a zoneC temperature sensor 641C. One or more of temperature sensors 641 a-641c may be located in building zone 670 and configured to measure thetemperature of building zone 670. However, the locations of temperaturesensors 641 a-641 c may be unknown to smart chiller 659 when smartchiller 659 is first installed. Smart chiller 659 can use the devicepairing techniques described with reference to FIG. 6A to determinewhich of temperature sensors 641 a-641 c is affected by refrigerationcircuit 660.

Smart chiller 659 is shown to include a communications interface 680 anda processing circuit 606. Communications interface 680 may be the sameor similar to communications interface 604, as described with referenceto FIG. 6A. Communications interface 680 can include wired or wirelesscommunications interfaces (e.g., jacks, antennas, transmitters,receivers, transceivers, wire terminals, etc.) for conducting datacommunications with external systems or devices (e.g., sensors 640, userdevices, supervisory controllers, etc.). Data communications viacommunications interface 680 can be direct (e.g., local wired orwireless communications) or via a communications network (e.g., a LAN, aWAN, the Internet, a cellular network, etc.). For example,communications interface 680 can include an Ethernet card and port forsending and receiving data via an Ethernet-based communications link ornetwork, a Wi-Fi transceiver for communicating via a wirelesscommunications network, and/or cellular or mobile phone communicationstransceivers for communicating via a cellular communications network.

Processing circuit 606 may include some or all of the components ofprocessing circuit 606 shown in FIG. 6A. For example, processing circuit606 is shown to include a processor 608 and memory 608. Processingcircuit 606 can be communicably connected to communications interface680 such that processing circuit 606 and the various components thereofcan send and receive data via communications interface 680. Processor608 can be implemented as a general purpose processor, an applicationspecific integrated circuit (ASIC), one or more field programmable gatearrays (FPGAs), a group of processing components, or other suitableelectronic processing components.

Memory 610 (e.g., memory, memory unit, storage device, etc.) can includeone or more devices (e.g., RAM, ROM, Flash memory, hard disk storage,etc.) for storing data and/or computer code for completing orfacilitating the various processes, layers and modules described in thepresent application. Memory 610 can be or include volatile memory ornon-volatile memory. Memory 610 can include database components, objectcode components, script components, or any other type of informationstructure for supporting the various activities and informationstructures described in the present application. In some embodiments,memory 610 is communicably connected to processor 608 via processingcircuit 606 and includes computer code for executing (e.g., byprocessing circuit 606 and/or processor 608) one or more processesdescribed herein.

Memory 610 may include some or all of the components of memory 610 shownin FIG. 6A. For example, memory 610 may include virtual actuationdevices 612, virtual sensors 614, time series database 616, dimensionalmismatch identifier 618, discrete cosine transformer 620, DCT quantizer622, similarity calculator 624, device pairing generator 626, and devicecontroller 628. When implemented in smart chiller 659, device pairinggenerator 626 can generate pairings between smart chiller 659 and one ormore of sensors 640. In other words, device pairing generator 626 candetermine which of sensors 640 is/are affected by smart chiller 659.Device controller 628 can use the device pairings and the sensorreadings from the affected sensors 640 to generate actuation signals forcompressor 662 and/or other components of refrigeration circuit 660.

Referring now to FIG. 6D, a block diagram illustrating another portionof HVAC system 600 in greater detail is shown, according to an exemplaryembodiment. HVAC system 600 is shown to include a smart thermostat 648,actuation devices 650, and a building zone 670. Smart thermostat 648 isshown to include sensors 640. Sensors 640 can include a temperaturesensor 641, humidity sensor 642, or any other type of sensor 640, asdescribed with reference to FIG. 6A. Sensors 640 can be configured tomeasure various environmental conditions or variables within buildingzone 670. For example, temperature sensor 641 can measure thetemperature of building zone 670, whereas humidity sensor 642 canmeasure the humidity of building zone 670. Smart thermostat 648 canindependently and automatically determine appropriate actuation signalsfor actuation devices 650 without requiring input from an externalcontroller.

Smart thermostat 648 can automatically determine which of actuationdevices 650 affect the environmental conditions of building zone 670 andcan operate actuation devices 650 to control the environmentalconditions measured by sensors 640. For example, actuation devices 650are shown to include several chillers 651, several heaters 652, andseveral air handling units 654. One or more of actuation devices 650 mayoperate to affect conditions within building zone 670. However, smartthermostat 648 may be unaware of such causal relationships when smartthermostat 648 is initially installed. Smart thermostat 648 can use thedevice pairing techniques described with reference to FIG. 6A todetermine which of actuation devices 650 can be operated to controlconditions within building zone 670.

Smart thermostat 648 is shown to include a communications interface 680and a processing circuit 606. Communications interface 680 may be thesame or similar to communications interface 604, as described withreference to FIG. 6A. Communications interface 680 can include wired orwireless communications interfaces (e.g., jacks, antennas, transmitters,receivers, transceivers, wire terminals, etc.) for conducting datacommunications with external systems or devices (e.g., actuation devices650, user devices, supervisory controllers, etc.). Data communicationsvia communications interface 680 can be direct (e.g., local wired orwireless communications) or via a communications network (e.g., a LAN, aWAN, the Internet, a cellular network, etc.). For example,communications interface 680 can include an Ethernet card and port forsending and receiving data via an Ethernet-based communications link ornetwork, a Wi-Fi transceiver for communicating via a wirelesscommunications network, and/or cellular or mobile phone communicationstransceivers for communicating via a cellular communications network.

Processing circuit 606 may include some or all of the components ofprocessing circuit 606 shown in FIG. 6A. For example, processing circuit606 is shown to include a processor 608 and memory 608. Processingcircuit 606 can be communicably connected to communications interface680 such that processing circuit 606 and the various components thereofcan send and receive data via communications interface 680. Processor608 can be implemented as a general purpose processor, an applicationspecific integrated circuit (ASIC), one or more field programmable gatearrays (FPGAs), a group of processing components, or other suitableelectronic processing components.

Memory 610 (e.g., memory, memory unit, storage device, etc.) can includeone or more devices (e.g., RAM, ROM, Flash memory, hard disk storage,etc.) for storing data and/or computer code for completing orfacilitating the various processes, layers and modules described in thepresent application. Memory 610 can be or include volatile memory ornon-volatile memory. Memory 610 can include database components, objectcode components, script components, or any other type of informationstructure for supporting the various activities and informationstructures described in the present application. In some embodiments,memory 610 is communicably connected to processor 608 via processingcircuit 606 and includes computer code for executing (e.g., byprocessing circuit 606 and/or processor 608) one or more processesdescribed herein.

Memory 610 may include some or all of the components of memory 610 shownin FIG. 6A. For example, memory 610 may include virtual actuationdevices 612, virtual sensors 614, time series database 616, dimensionalmismatch identifier 618, discrete cosine transformer 620, DCT quantizer622, similarity calculator 624, device pairing generator 626, and devicecontroller 628. When implemented in smart thermostat 648, device pairinggenerator 626 can generate pairings between sensors 640 and one or moreof actuation devices 650. In other words, device pairing generator 626can determine which of actuation devices 650 affect the variables orconditions measured by sensors 640. Device controller 628 can use thedevice pairings and the sensor readings from sensors 640 to generateactuation signals for actuation devices 650.

Example Graphs

Referring now to FIG. 7 a graph 700 illustrating the different types ofsignals evaluated by controller 602 is shown, according to an exemplaryembodiment. In graph 700, line 702 represents an actuation signal t(x),line 706 represents a sensor response signal r(x), and line 710represents a baseline sensor signal a(x). The actuation signal t(x) canbe provided to one or more of actuation devices 650, as previouslydescribed. In the example shown in FIG. 7, the actuation signal t(x) isa setpoint signal (e.g., a temperature setpoint) which can be providedas an input to actuation devices 650 (e.g., controllable HVAC equipment)which operate to affect the temperature of a building zone. In someembodiments, the actuation signal t(x) is a predetermined test signalwhich differs from the normal actuation signals provided to actuationdevices 650. For example, the actuation signal t(x) is shown as a stepincrease from 68° F. to 72° F., which is held for a predetermined timeperiod, followed by a step decrease from 72° F. back to 68° F. Theactuation signal t(x) causes actuation devices 650 to increase an amountof heating provided to the building zone, which causes the temperatureof the building zone to increase.

The sensor response signal r(x) can be received from one or more ofsensors 640, as previously described. In the example shown in FIG. 7,the sensor response signal r(x) is a measured temperature signalreceived from a temperature sensor located in the building zonecontrolled by actuation devices 650. The temperature of the buildingzone begins to increase shortly after the actuation signal t(x) isincreased, which results in an increase in the sensor response signalr(x). Similarly, the temperature of the building zone begins to decreaseshortly after the actuation signal t(x) is decreased, which results in adecrease in the sensor response signal r(x). The time delay between theactuation signal t(x) and the sensor response signal r(x) is shown asdelay time Δw.

The baseline sensor signal a(x) can be received from the same sensor 640which provides the sensor response signal r(x). However, the baselinesensor signal a(x) indicates the sensor readings from the sensor 640from a time period before the actuation signal t(x) is provided toactuation devices 650. The signals shown in graph 700 are characteristicof a sensor 640 and an actuation device 650 which have a causalrelationship with each other. For example, the sensor response signalr(x) closely matches the actuation signal t(x), whereas the baselinesensor signal a(x) is significantly different from both the actuationsignal t(x) and the sensor response signal r(x). In other words, thesensor response signal r(x) is correlated with the actuation signalt(x), which indicates a causal relationship between the correspondingactuation device 650 and sensor 640.

Still referring to FIG. 7, graph 700 is shown to include a line 704representing the discrete cosine transform (DCT) of the actuation signalt(x), a line 708 representing the DCT of the sensor response signalr(x), and a line 712 representing the DCT of the baseline sensor signala(x). Discrete cosine transformer 620 can be configured to generate DCTsof the various time series signals received or generated by controller602, as previously described. In graph 700, the DCTs represented bylines 704, 708, and 712 are continuous functions (i.e., summations ofcosine functions) which results in a smooth curves for each of lines704, 708, and 712. Advantageously, the DCT functions reduce any noisepresent in the signals t(x), r(x), and a(x), and can be evaluated at anypoint. In some embodiments, controller 602 compares the DCTs of theactuation signal t(x), sensor response signal r(x), and baseline sensorsignal a(x) rather than the original signals to determine whether acausal relationship exists between a particular sensor 640 and aparticular actuation device 650.

Referring now to FIG. 8 a graph 800 illustrating the dimensionalmismatch handling performed by controller 602 is shown, according to anexemplary embodiment. In graph 800, line 802 represents samples ofactuation signal time series t(x) provided to one or more of actuationdevices 650, whereas line 804 represents samples of a sensor responsetime series r(x) received from one or more of sensors 640. The actuationsignal time series t(x) and the sensor response time series r(x) coverthe same time period. However, sampling rate used to generate thesamples of the sensor response time series r(x) is twice the samplingrate used to generate the samples of the actuation time series t(x),which results in twice as many samples of the sensor response timeseries r(x) being collected during the same time period. In other words,the sensor response time series r(x) includes twice as many samples(e.g., 24 samples) as the actuation signal time series t(x) (e.g., 12samples).

In order to compare the actuation signal time series t(x) and the sensorresponse time series r(x), controller 602 can generate DCTs of each timeseries. As described with reference to FIG. 6A, discrete cosinetransformer 620 can generate DCT coefficients for the actuation signaltime series t(x) using the following equation:

${T(k)} = {\sum\limits_{i = 0}^{N - 1}{t_{i}{\cos \left\lbrack {\frac{\pi}{N}\left( {i + \frac{1}{2}} \right)k} \right\rbrack}}}$k = 0, …  , N − 1

where T(k) is the kth coefficient of the DCT of the actuation signaltime series t(x), t_(i) is the ith sample of the actuation signal timeseries t(x), and N is the number of samples of the actuation signal timeseries t(x). Discrete cosine transformer 620 can generate an array T ofthe DCT coefficients (e.g., T=[T(0), T(1), T(2), . . . , T(N−2),T(N−1)]) where the length of the array T is the same as the number ofsamples N of the actuation signal time series t(x) (e.g., 12 samples).

Similarly, discrete cosine transformer 620 can generate DCT coefficientsfor the sensor response time series r(x) using the following equation:

${R(k)} = {\sum\limits_{i = 0}^{M - 1}{r_{i}{\cos \left\lbrack {\frac{\pi}{M}\left( {i + \frac{1}{2}} \right)k} \right\rbrack}}}$k = 0, …  , M − 1

where R(k) is the kth coefficient of the DCT of the sensor response timeseries r(x), r_(i) is the ith sample of the sensor response time seriesr(x), and M is the number of samples of the sensor response time seriesr(x). Discrete cosine transformer 620 can generate an array R of the DCTcoefficients (e.g., R=[R(0), R(1), R(2), . . . , R(M−2), R(M−1)]) wherethe length of the array R is the same as the number of samples M of thesensor response time series r(x) (e.g., 24 samples).

To make the arrays T and R the same dimension, DCT quantizer 622 canperform a quantization process. In some embodiments, DCT quantizer 622performs the quantization process using a predetermined quantizationlevel. The quantization level may define the number of the DCTcoefficients in each array T and R which are retained (i.e., not filledwith zeros). For example, a quantization level of twelve may retain thefirst twelve DCT coefficients in each array T and R while the remainingDCT coefficients are filled with zeros. In the example shown in FIG. 8,DCT quantizer 622 applies a quantization level of twelve to each array Tand R of DCT coefficients. This quantization level has no effect on thearray T since the array T only contains 12 DCT coefficients. However,applying a quantization level of twelve to the array R has the effect offilling the final twelve DCT coefficients with zeros such that only thefirst twelve DCT coefficients in array R are retained.

Still referring to FIG. 8, graph 800 is shown to include a line 806representing the inverse DCT of the actuation signal time series t(x)and a line 808 representing the inverse DCT of the sensor response timeseries r(x). Lines 806-808 illustrate the similarity between thequantized DCT functions based on the actuation signal time series t(x)and the sensor response time series r(x). As discussed with reference toFIG. 6A, similarity calculator 624 can evaluate the similarity betweentwo or more DCT functions without reconstructing the inverse DCT (e.g.,by calculating a distance between the DCT coefficients). However, theinverse DCTs are shown in graph 800 to illustrate how controller 602 canreduce the dimension of the sensor response time series r(x) to matchthe dimension of the actuation signal time series t(x) (or vice versa)by performing a discrete cosine transform and applying a quantizationprocess.

Flow Diagrams

Referring now to FIG. 9, a flowchart of a process 900 for establishingdevice pairings between sensors and actuation devices is shown,according to an exemplary embodiment. In some embodiments, process 900is performed by one or more components of controller 602, as describedwith reference to FIG. 6A. Process 900 can be used in a building and/ora building HVAC system to automatically establish device pairingsbetween various sensors and causally-related actuation devices. Thedevice pairings can then be used to automatically generate control loopsfor use in controlling the actuation devices.

Process 900 is shown to include collecting baseline sensor measurementsfrom a plurality of sensors (step 902). The sensors can include some orall of sensors 640, as described with reference to FIG. 6A. For example,the sensors can include temperature sensors, humidity sensors, airflowsensors, lighting sensors, pressure sensors, voltage sensors, or anyother type of sensor in a building and/or a building HVAC system. Thesensors can be distributed throughout a building and configured tomeasure various environmental conditions at different locations in thebuilding. For example, one temperature sensor can be located in a firstzone of the building and configured to measure the temperature of thefirst zone, whereas another temperature sensor can be located in asecond zone of the building and configured to measure the temperature ofthe second zone. Similarly, the sensors can be distributed throughout aHVAC system and configured to measure conditions at different locationsin the HVAC system. For example, one of temperature sensor can be asupply air temperature sensor configured to measure the temperature ofthe airflow provided to a building zone from an AHU, whereas anothertemperature sensor can be a return air temperature sensor configured tomeasure the temperature of the airflow returning from the building zoneto the AHU.

The baseline sensor measurements may be received from the plurality ofsensors during a baseline time period before an actuation signal or testsignal is provided to actuation devices 650. In some embodiments, thebaseline temperature measurements are used to generate a baseline sensorsignal time series a(x). The baseline sensor signal time series a(x) mayindicate the average sensor readings from the plurality of sensorsbefore the actuation signal or test signal is provided to the actuationdevices 650.

In some embodiments, step 902 includes storing the baseline sensorsignal time series a(x) in a time series database (e.g., time seriesdatabase 616). The time series database can store the baseline sensormeasurements from each of the plurality of sensors as separate baselinesensor signal time series a(x) as shown in the following equation:

a(x)={a ₁ ,a ₂ ,a ₃ , . . . ,a _(P-1) ,t _(P)}

where each element a_(i) of the baseline sensor signal time series a(x)is the value of a baseline sensor signal at a particular time (i.e., asample of the baseline sensor signal) and P is the total number ofelements in the baseline sensor signal time series a(x).

Still referring to FIG. 9, process 900 is shown to include providing anactuation signal to an actuation device (step 904). The actuation devicecan include any of actuation devices 650, as described with reference toFIG. 6A. For example, the actuation device can include a chiller,heater, valve, air handling unit (AHU), damper, actuator, and/or anyother physical device configured to affect a variable state or conditionin a building or building HVAC system. The actuation device can includeany of the equipment in building 10, HVAC system 100, waterside system200, airside system 300, BMS 400, and/or BMS 500, as described withreference to FIGS. 1-5. The actuation device can operate to affectvarious building conditions including temperature, humidity, airflow,lighting, air quality, power consumption, or any other variable state orcondition in a building. In some embodiments, the actuation devicereceives the actuation signal from a controller (e.g., controller 602)via an output module.

In some embodiments, the actuation signal is a control signal for theactuation devices (e.g., operating setpoints, on/off commands, etc.).For example, the actuation signal can include commands to activate ordeactivate the actuation device and/or commands to operate the actuationdevice a variable capacity (e.g., operate at 20% capacity, 40% capacity,etc.). If the actuation device is a device with a variable position(e.g., a valve, a damper, an actuator, etc.) the actuation signal caninclude position setpoints for the actuation device. The positionsetpoints can include commands to move to a fully closed position, a 50%open position, a fully open position, or any intermediate position. Insome embodiments, the actuation signal is a predetermined test signalwhich differs from the normal actuation signals provided to theactuation device.

In some embodiments, the actuation signal is provided directly to theactuation device from the controller and used to adjust a physicaloperation of the actuation device (e.g., if the controller directlycontrols the actuation device). In other embodiments, the actuationsignal is provided to an intermediate controller for the actuationdevices. For example, a supervisory controller can provide a setpoint toa local controller for the actuation device. The local controller canthen generate control signals for the actuation devices to achieve thesetpoint received from the supervisory controller.

In some embodiments, step 904 includes using the actuation signal togenerate an actuation signal time series t(x). The actuation signal timeseries t(x) can be stored in the time series database. The time seriesdatabase can store each time series of actuation signal values as anactuation signal time series t(x) as shown in the following equation:

t(x)={t ₁ ,t ₂ ,t ₃ , . . . ,t _(N-1) ,t _(N)}

where each element t_(i) of the actuation signal time series t(x) is thevalue of the actuation signal at a particular time (i.e., a sample ofthe actuation signal) and N is the total number of elements in theactuation signal time series t(x).

Still referring to FIG. 9, process 900 is shown to include recordingsensor response signals from the plurality of sensors in response to theactuation signal (step 906). The sensor response signals indicate theeffect of the actuation device on the variables measured by theplurality of sensors. If a causal relationship exists between theactuation device and a particular sensor (i.e., the actuation device canaffect the value measured by the sensor), the sensor response signal maychange in response to providing the actuation signal to the actuationdevice. However, if no causal relationship exists between the actuationdevice and the sensor (i.e., the actuation device is not capable ofaffecting the value measured by the sensor), the sensor response signalmay not change in response to providing the actuation signal to theactuation device.

In some embodiments, step 906 includes generating time series of sensorresponse values. Each time series of sensor response values can bestored in the time series database as a sensor response time series r(x)as shown in the following equation:

r(x)={r ₁ ,r ₂ ,r ₃ , . . . ,r _(M-1) ,r _(M)}

where each element r_(i) of the sensor response time series r(x) is thevalue of the sensor response signal at a particular time (i.e., a sampleof the sensor response signal) and M is the total number of elements inthe sensor response time series r(x).

Process 900 is shown to include determining a similarity between theactuation signal and each sensor response signal (step 908). In someembodiments, step 908 includes calculating a similarity metric orsimilarity score indicating a similarity (e.g., a distance) between theactuation signal time series t(x) and each of the sensor response timeseries r(x). The following equation can be used to calculate thesimilarity metric between the actuation signal time series t(x) and agiven sensor response time series r(x):

${d\left( {{t(x)},{r(x)}} \right)} = {\sum\limits_{i = 1}^{i = N}\frac{\sqrt{\left( {t_{i} - r_{i}} \right)^{2}}}{\delta_{i}}}$

where t_(i) is the ith sample in the actuation signal time series t(x),r_(i) is the ith sample in the sensor response time series r(x), δ_(i)is the standard deviation of the ith samples, and N is the number ofsamples in each time series. Low values of the similarity metric d(t(x),r(x)) indicate a greater similarity (i.e., a lower distance between timeseries), whereas high values of the similarity metric d(t(x), r(x))indicate a lesser similarity (i.e., a greater distance between timeseries). Step 908 can include calculating a similarity metric for eachpairing of the actuation signal time series t(x) with one of the sensorresponse time series r(x).

The previous equation for calculating the similarity metric can be usedif both the actuation signal time series t(x) and the sensor responsetime series r(x) have the same number of samples. However, if theactuation signal time series t(x) and the sensor response time seriesr(x) have different numbers of samples, additional processing may berequired. For example, step 908 may include performing a discrete cosinetransformation (DCT) of the actuation signal time series t(x) and eachsensor response time series r(x) to generate sets of DCT coefficientsfor each time series. The sets of DCT coefficients can then bequantized, as described with reference to FIG. 6A, to produce arrays ofcompressed DCT coefficients for each time series. The similarity metricscan then be calculated based on the compressed DCT coefficientsgenerated for each time series.

The compressed DCT coefficients generated for the actuation signal timeseries t(x) can be represented by an array T, and the compressed DCTcoefficients generated for a given sensor response time series r(x) canbe represented by an array R. The arrays T and R can be particularinstances of the compressed array C(k) generated by DCT quantizer 622for the actuation signal time series t(x) and the sensor response timeseries r(x), respectively. Each array T and R can include apredetermined number N of DCT coefficients, defined by the quantizationlevel applied by DCT quantizer 622. Examples of arrays T and R are asfollows:

T=

t ₁ ,t ₂ , . . . t _(N)

R=

r ₁ ,r ₂ , . . . ,r _(N)

Step 908 can include generating an array T of DCT coefficients for theactuation signal time series t(x) and an array R of DCT coefficients foreach of the sensor response time series r(x).

In some embodiments, step 908 includes calculating a similarity metricfor the source time series t(x) and r(x) based on the correspondingarrays T and R of compressed DCT coefficients. Step 908 can includecalculating the similarity metric using the following equation:

${d\left( {T,R} \right)} = {\sum\limits_{i = 1}^{i = N}\frac{\sqrt{\left( {t_{i} - r_{i}} \right)^{2}}}{\delta_{i}}}$

where t_(i) is the ith DCT coefficient in the array T based on theactuation signal time series t(x), r_(i) is the ith DCT coefficient inthe array R based on the sensor response time series r(x), δ_(i) is thestandard deviation of the ith DCT coefficients, and N is the number ofDCT coefficients in each array T and R. Low values of the similaritymetric d(T, R) indicate a greater similarity, whereas high values of thesimilarity metric d(T, R) indicate a lesser similarity. Step 908 caninclude calculating a similarity metric for each pairing of the array Tbased on the actuation signal time series t(x) with one of the arrays Rbased on one of the sensor response time series r(x).

Still referring to FIG. 9, process 900 is shown to include identifyingthe sensor response signal with the greatest similarity to the actuationsignal and the corresponding sensor (step 910). Step 910 can includecomparing the similarity metrics d(T, R) calculated for the actuationdevice in combination with each of the plurality of sensors. Eachsimilarity metric d (T, R) indicates the similarity (i.e., thecloseness) between the actuation signal time series t(x) associated withthe actuation device and the sensor response time series r(x) associatedwith one of the sensors. For example, the similarity metric d(T₁, R₁)may indicate the similarity between the actuation device and a firstsensor of the plurality of sensors (corresponding to array R₁), whereasthe similarity metric d(T₁, R₂) may indicate the similarity between theactuation device and a second sensor of the plurality of sensors(corresponding to array R₂).

Step 910 can include identifying all of the similarity metricsassociated with the actuation device (e.g., d(T₁, R₁), . . . , d(T₁,R_(P)), where P is the total number of sensors and/or sensor responsetime series r(x)). In some embodiments, step 910 includes determiningwhich of the identified similarity metrics is the lowest. The lowestsimilarity metric may indicate the closest match between the actuationsignal time series t(x) associated with the actuation device and thesensor response time series r(x) associated with one of the sensors. Inother embodiments, step 910 can include determining which of theidentified similarity metrics is the highest. For example, othertechniques for calculating the similarity metric may produce largersimilarity metrics when two time series match more closely. Regardlessof how the similarity metric is calculated, step 910 can includeidentifying the similarity metric which indicates the closest matchbetween the actuation signal time series t(x) and the correspondingsensor response time series r(x).

If the actuation device has the same similarity metric with multiplesensors (e.g., d(T₁, R₁)=d(T₁, R₂)), step 910 can include examining thetime delay Δw between the actuation signal time series t(x) associatedwith the actuation device and sensor response time series r(x)associated with each of the sensors. The time delay Δw may indicate thedelay between the time w₁ at which the actuation signal is applied tothe actuation device and the time w₂ at which the effects of theactuation signal are evident in each sensor response (e.g., Δw=w₂−w₁).Step 910 can include identifying the sensor and/or sensor response timeseries r(x) with the lowest time delay Δw relative to the actuationsignal time series t(x).

Still referring to FIG. 9, process 900 is shown to include establishinga device pairing between the actuation device and the identified sensor(step 912). The device pairing can include the actuation device and thesensor identified in step 910. The device pairing between the actuationdevice and the sensor indicates that the actuation device is capable ofaffecting the value measured by the sensor. In some embodiments step 910includes using the device pairing to automatically generate and storecausal relationships between the actuation device and the identifiedsensor.

Process 900 is shown to include using the device pairing to generate andprovide control signals to the actuation device (step 914). Step 914 caninclude using the device pairing to create a feedback control loop forHVAC system 600. The feedback control loop can receive a feedback signalfrom the identified sensor and can provide a control signal to theactuation device. Step 914 can include using the device pairing todefine the sensor and actuation device in the control loop. For example,step 914 can include creating a control loop which receives a feedbacksignal from the sensor in the device pairing and provides a controlsignal to the actuation device in the device pairing. Step 914 caninclude mapping the sensor readings from the sensor in the devicepairing to the feedback signal in the control loop. Similarly, step 914can include mapping the actuation signals provided to the actuationdevice in the device pairing to the control signal in the control loop.

Step 914 can include using the feedback control loop to generateactuation signals for the actuation device. Step 914 can include usingstate-based algorithms, extremum seeking control (ESC) algorithms,proportional-integral (PI) control algorithms,proportional-integral-derivative (PID) control algorithms, modelpredictive control (MPC) algorithms, or any other type of controlmethodology to generate the actuation signals for the actuation devicebased on the sensor readings. For example, if the sensor reading fromthe sensor indicates that the temperature of a particular building zoneis below a temperature setpoint for the building zone, step 914 caninclude providing an actuation signal to the actuation device toincrease the amount of heating provided to the building zone.Advantageously, process 900 can be performed to automatically establishrelationships between various actuation devices and sensors based on thedevice pairings to allow controller 602 to determine which of theactuation devices can be operated to affect a given sensor reading.

Referring now to FIG. 10, a flowchart of a process 1000 for handlingdimensional mismatches between actuation signal time series and sensorresponse time series is shown, according to an exemplary embodiment. Insome embodiments, process 1000 is performed by one or more components ofcontroller 602, as described with reference to FIG. 6A. Process 1000 canbe used in a building and/or a building HVAC system to automaticallyestablish device pairings between various sensors and actuation deviceswhen the time series have different sampling rates and/or differentnumbers of samples. The device pairings can then be used toautomatically generate control loops for use in controlling theactuation devices.

Process 1000 is shown to include performing a discrete cosinetransformation (DCT) of actuation signal time series t(x) and sensorresponse time series r(x) to generate sets of DCT coefficients (step1002). In some embodiments, step 1002 is performed by discrete cosinetransformer 620, as described with reference to FIG. 6A. Step 1002 caninclude performing a DCT for each actuation signal time series t(x) andsensor response time series r(x). A DCT expresses a finite sequence ofdata points in terms of a sum of cosine functions oscillating atdifferent frequencies. In particular, a DCT is a Fourier-relatedtransform similar to the discrete Fourier transform (DFT), but usingonly real numbers. There are eight standard DCT variants, commonlyreferred to as DCT-I, DCT-II, DCT-III, DCT-IV, DCT-V, DCT-VI, DCT-VII,and DCT-VIII. One of these variants (i.e., DCT-II) is discussed indetail below. However, it should be understood that step 1002 can useany standard or non-standard DCT variant in other embodiments.

In some embodiments, step 1002 includes performing a DCT for eachactuation signal time series t(x) using the following equation:

${T(k)} = {\sum\limits_{i = 0}^{N - 1}{t_{i}{\cos \left\lbrack {\frac{\pi}{N}\left( {i + \frac{1}{2}} \right)k} \right\rbrack}}}$k = 0, …  , N − 1

where T(k) is the kth coefficient of the DCT of the actuation signaltime series t(x), t_(i) is the ith sample of the actuation signal timeseries t(x), and N is the number of samples of the actuation signal timeseries t(x). Step 1002 can include generating an array T of the DCTcoefficients (e.g., T=[T(0), T(1), T(2), . . . , T(N−2), T(N−1)]) wherethe length of the array T is the same as the number of samples N of theactuation signal time series t(x).

Similarly, step 1002 can include performing a DCT for each sensorresponse time series r(x) using the following equation:

${R(k)} = {\sum\limits_{i = 0}^{M - 1}{r_{i}{\cos \left\lbrack {\frac{\pi}{M}\left( {i + \frac{1}{2}} \right)k} \right\rbrack}}}$k = 0, …  , M − 1

where R(k) is the kth coefficient of the DCT of the sensor response timeseries r(x), r_(i) is the ith sample of the sensor response time seriesr(x), and M is the number of samples of the sensor response time seriesr(x). Step 1002 can include generating an array R of the DCTcoefficients (e.g., R=[R(0), R(1), R(2), . . . , R(M−2), R(M−1)]) wherethe length of the array R is the same as the number of samples M of thesensor response time series r(x).

The following example illustrates the result of applying DCT to an inputtime series X(n). The input time series X(n) can be an actuation signaltime series t(x) or a sensor response time series r(x) as previouslydescribed. The samples of the input time series X(n) are shown in thefollowing array:

X(n)=[1.00,1.70,2.00,2.00,4.30,4.50,3.00,3.00,2.30,2.20,2.20,2.30]

where the input time series X(n) includes twelve time series valuesX(1), . . . ,X(12). Applying DCT to the input time series X(n) resultsin a set of DCT coefficients, shown in the following array:

Y(k)=[8.80,−0.57,−2.65,−1.15,0.81,0.52,−0.20,−0.93,−0.63,0.32,0.50,−0.06]

where the array of DCT coefficients Y(k) includes twelve DCTcoefficients Y(1), . . . , Y(12).

Still referring to FIG. 10, process 1000 is shown to include identifyinga dimensional mismatch between the actuation signal time series t(x) andthe sensor response time series r(x) (step 1004). In some embodiment,step 1004 is performed by dimensional mismatch identifier 618, asdescribed with reference to FIG. 6A. A dimensional mismatch may occurwhen two time series have a different number of samples and/or samplingrates. In some embodiments, step 1004 includes determining the size ofeach time series. For example, step 1004 can include determining thenumber of samples N in the actuation signal time series t(x) and thenumber of samples M in the sensor response time series r(x). The numberof samples N and M can be determined by counting the number of samplesin the original time series t(x) and r(x) or the number of elements ineach of the arrays of DCT coefficients T and R. Step 1004 can includeidentifying a dimensional mismatch in response to a determination thatthe number of samples N in the actuation signal time series t(x) or thearray T is different from the number of samples M in the sensor responsetime series r(x) or the array R (i.e., N≠M).

In some embodiments, step 1004 includes determining the sampling rate ofeach time series. In some embodiments, the sampling rate of a timeseries may be stored as metadata associated with the time series in timeseries database 616. Step 1004 can include determining the sampling rateof a time series by reading the sampling rate from the metadata in timeseries database 616. In other embodiments, step 1004 includescalculating the sampling rate for one or more time series based on thesize of the time series and the range of time spanned by the timeseries.

Step 1004 can include identifying a start time and an end time for thetime series by reading the timestamps associated with the first and lastdata samples in the time series. Step 1004 can include calculating thesampling rate by dividing the size of the time series by the differencebetween the end time and the start time, as shown in the followingequation:

${sampling\_ rate} = \frac{{size\_ of}{\_ timeseries}}{{end\_ time} - {start\_ time}}$

where size_of_timeseries is the number of samples M or N in the timeseries, end_time is the timestamp associated with the last sample in thetime series, start_time is the timestamp associated with the firstsample in the time series, and sampling_rate is the sampling rate of thetime series, expressed as the number of samples per unit time (e.g., 0.8samples/hour). Step 1004 can include identifying a dimensional mismatchin response to a determination that two time series have differentsampling rates.

In some embodiments, step 1004 includes correcting a dimensionalmismatch by increasing the number of samples of the time series with thefewer number of samples (e.g., by interpolating between samples). Inother embodiments, step 1004 includes correcting dimensional mismatch byreducing the number of samples of the time series with the greaternumber of samples (e.g., by discarding extra samples). In otherembodiments, step 1004 merely identifies a dimensional mismatch which iscorrected by subsequent steps of process 1000.

Still referring to FIG. 10, process 1000 is shown to include applying aquantization to the sets of DCT coefficients generated in step 1002 toequalize the number of DCT coefficients in each set (step 1006). In someembodiments, step 1006 is performed by DCT quantizer 622, as describedwith reference to FIG. 6A. Step 1006 can be performed in response to adetermination in step 1004 that a dimensional mismatch exists betweenthe actuation signal time series t(x) and the sensor response timeseries r(x).

As described above, the DCT process performed in step 1002 converts aninput data time series X(n) into a sum of cosine functions whichoscillate at different frequencies. The cosine function with the lowestfrequency is typically first in the summation, followed by cosinefunctions with successively higher frequencies. Accordingly, the DCTcoefficient which occurs first in the array of DCT coefficients Y(k)represents the magnitude of the lowest frequency cosine function. Eachof the following DCT coefficients represents the magnitude of a cosinefunction with a successively higher oscillation frequency.

Step 1006 can include applying a quantization process to the sets of DCTcoefficients by filling some of the higher frequency DCT coefficientswith zeros. This has the effect of removing some of the higher frequencycomponents (i.e., cosine functions) from the summation while retainingthe lower frequency components. In some embodiments, step 1006 includesperforming the quantization process using a predetermined quantizationlevel. The quantization level may define the number of the DCTcoefficients which are retained (i.e., not filled with zeros). Forexample, a quantization level of six may retain the DCT coefficientsapplied to the six lowest frequency cosine functions (e.g., the firstsix DCT coefficients in the array) while the remaining DCT coefficientsare filled with zeros.

The following example illustrates the result of a quantization processwhich can be performed in step 1006. Step 1006 can include modifying thearray of DCT coefficients Y(k) shown above to form the followingquantized array QY(k):

QY(k)=[8.80,−0.57,−2.65,−1.15,0.81,0.52,0.00,0.00,0.00,0.00,0.00,0.00]

In this example, a quantization level of six is applied, meaning thatonly the first six DCT coefficients are retained from the original arrayof DCT coefficients Y(k). The remaining DCT coefficients are filled withzeros. True compression can be achieved by not storing the zeros. Forexample, step 1006 can include storing the following compressed arrayC(k):

C(k)=[8.80,−0.57,−2.65,−1.15,0.81,0.52]

in which the coefficients filled with zeros are discarded to produce acompressed array with a length equal to the quantization level applied.In this example, the compressed array C(k) has a length of six resultingfrom the use of a quantization level of six. In various embodiments,step 1006 can use a quantization level of six or any other quantizationlevel to produce compressed arrays of various lengths.

In some embodiments, step 1006 includes automatically determining thequantization level to apply based on the number of samples in each ofthe original actuation signal time series t(x) and sensor response timeseries r(x). As described above, the number of DCT coefficients producedin step 1002 for a given input time series X(n) may be equal to thenumber of samples in the time series X(n) prior to performing DCT. Forexample, an input time series X₁(n) with twelve samples may result intwelve DCT coefficients in the resultant DCT coefficient array Y₁(k),whereas an input time series X₂(n) with ten samples may result in tenDCT coefficients in the resultant DCT coefficient array Y₂ (k). In someembodiments, step 1006 includes identifying the actuation signal timeseries t(x) or sensor response time series r(x) with the fewest samplesand applies a quantization level equal to the number of samples in theidentified time series.

In some embodiments, step 1006 includes applying the same quantizationlevel to the sets of DCT coefficients corresponding to each of theoriginal actuation signal time series t(x) and sensor response timeseries r(x). Using the same quantization level for each of the originaltime series may result in the same number of compressed DCT coefficientsbeing stored for each of the original actuation signal time series t(x)and sensor response time series r(x). In some embodiments, the number ofstored DCT coefficients is equal to the number of samples in theoriginal time series with the fewest samples. Advantageously, thisallows for direct comparison of the DCT coefficients in the compressedarrays C(k) generated for each of the original time series withoutrequiring decompression, interpolation, synchronization, or otherprocessing steps after the compressed arrays C(k) are generated.

In some embodiments, step 1006 includes generating a compressed timeseries T_(α) based on each compressed array of DCT coefficients. Step1006 can include storing the compressed time series T_(α) using thefollowing data structure:

T _(α)=

α,δ,ρ,κ,

ψ

,

υ₁,υ₂, . . . υ_(p)

where α is the time series ID of the source time series (e.g., theactuation signal time series t(x) or sensor response time series r(x)),δ is the dimension of the source time series (e.g., the number ofsamples in the source time series), ρ is the quantization level appliedby in step 1006, κ is a pointer for metadata,

ψ

indicates the start time and end time of samples in the source timeseries, and

υ₁, υ₂, . . . υ_(p)

is the array of compressed DCT coefficients. An example of a compressedtime series stored using this data structure is as follows:

T ₂₀₃=

203,12,6,

2016:10:05:12:00:00,2016:10:05:13:00:00

,

8.80,−0.57,−2.65,−1.15,0.81,0.52

where 203 is the time series ID of the source time series, 12 is size ofthe source time series (e.g., 12 samples in the source time series), 6is the quantization level applied by in step 1006, 2016:10:05:12:00:00is the start time of the source time series (e.g., the timestamp of theearliest sample in the source time series), 2016:10:05:13:00:00 is theend time of the source time series (e.g., the timestamp of the latestsample in the source time series), and the array

8.80, −0.57, −2.65, −1.15, 0.81, 0.52

includes the compressed DCT coefficients generated in step 1006.

Still referring to FIG. 10, process 1000 is shown to include determininga similarity between the actuation signal time series t(x) and thesensor response time series r(x) based on the quantized sets of DCTcoefficients (step 1008). The quantized DCT coefficients generated forthe actuation signal time series t(x) can be represented by an array T,and the compressed DCT coefficients generated for a given sensorresponse time series r(x) can be represented by an array R. The arrays Tand R can be particular instances of the compressed array C(k) generatedin step 1006 for the actuation signal time series t(x) and the sensorresponse time series r(x), respectively. Each array T and R can includea predetermined number N of DCT coefficients, defined by thequantization level applied in step 1006. Examples of arrays T and R areas follows:

T=

t ₁ ,t ₂ , . . . t _(N)

R=

r ₁ ,r ₂ , . . . ,r _(N)

In some embodiments, step 1008 includes calculating a similarity metricfor the source time series t(x) and r(x) based on the correspondingarrays T and R of compressed DCT coefficients. Step 1008 can includecalculating the similarity metric using the following equation:

${d\left( {T,R} \right)} = {\sum\limits_{i = 1}^{i = N}\frac{\sqrt{\left( {t_{i} - r_{i}} \right)^{2}}}{\delta_{i}}}$

where t_(i) is the ith DCT coefficient in the array T based on theactuation signal time series t(x), r_(i) is the ith DCT coefficient inthe array R based on the sensor response time series r(x), δ_(i) is thestandard deviation of the ith DCT coefficients, and N is the number ofDCT coefficients in each array T and R. Low values of the similaritymetric d(T, R) indicate a greater similarity, whereas high values of thesimilarity metric d (T, R) indicate a lesser similarity.

Process 1000 is shown to include establishing a device pairing betweenan actuation device and a sensor based on the similarity between thecorresponding time series (step 1010). The actuation device in step 1010may be the specific actuation device to which the actuation signal t(x)is provided, whereas the sensor in step 1010 may be the specific sensorfrom which the sensor response signal r(x) is received. In someembodiments, step 1010 includes comparing the similarity metric d(T, R)calculated in step 1008 to a threshold value. Step 1010 can includegenerating a device pairing between the sensor and the actuation deviceif the similarity metric d(T, R) is less than the threshold value (e.g.,d(T, R)<threshold). The threshold value can be a predefined value or acalculated value (e.g., a standard deviation of the DCT coefficients).

In some embodiments, the threshold value is a similarity metric betweenthe actuation signal time series t(x) and a baseline (e.g., average)sensor signal time series a(x) over a predetermined time period. Thebaseline sensor signal time series a(x) can indicate the average sensorresponse from the sensor before the actuation signal is applied to theactuation device (e.g., baseline sensor readings), whereas the sensorresponse time series r(x) can indicate the sensor response from the samesensor after the actuation signal is applied to the actuation device. Ifthe actuation device affects the sensor, the actuation signal timeseries t(x) is expected to be more similar to the sensor response timeseries r(x) than the baseline sensor signal time series a(x).Accordingly, the similarity metric d(T, R) between the actuation signaltime series t(x) and the sensor response time series r(x) is expected tobe lower (i.e., more similar) than the similarity metric d(T, A) betweenthe actuation signal time series t(x) and the baseline sensor signaltime series a(x).

In some embodiments, step 1010 includes generating DCT coefficients andcompressed DCT coefficients for each baseline sensor signal time seriesa(x), sensor response time series r(x), and actuation signal time seriest(x). Step 1010 can include calculating a similarity metric d(T, R)between the actuation signal time series t(x) and the sensor responsetime series r(x) associated with the temperature sensor. Step 1010 canalso include calculating a baseline similarity metric d(T, A) betweenthe actuation signal time series t(x) and baseline sensor signal timeseries a(x) associated with the same temperature sensor. Step 1010 caninclude generating a device pairing between the sensor and the actuationdevice if the similarity metric d(T, R) for the combination of thesensor and the actuation device indicates a greater similarity (e.g., alower similarity metric) than the baseline similarity metric d(T, A) forthe sensor and the actuation device.

In some embodiments, step 1010 includes comparing the similarity metricsd(T, R) calculated for the actuation device in combination with each ofa plurality of sensors. Each similarity metric d(T, R) indicates thesimilarity (i.e., the closeness) between the actuation signal timeseries t(x) associated with the actuation device and the sensor responsetime series r(x) associated with one of the sensors. For example, thesimilarity metric d(T₁, R₁) may indicate the similarity between theactuation device and a first sensor of the plurality of sensors(corresponding to array R₁), whereas the similarity metric d(T₁, R₂) mayindicate the similarity between the actuation device and a second sensorof the plurality of sensors (corresponding to array R₂).

Step 1010 can include identifying all of the similarity metricsassociated with the actuation device (e.g., d(T₁, R₁), . . . , d(T₁,R_(p)), where P is the total number of sensors and/or sensor responsetime series r(x)). In some embodiments, step 1010 includes determiningwhich of the identified similarity metrics is the lowest. The lowestsimilarity metric may indicate the closest match between the actuationsignal time series t(x) associated with the actuation device and thesensor response time series r(x) associated with one of the sensors. Inother embodiments, step 1010 can include determining which of theidentified similarity metrics is the highest. For example, othertechniques for calculating the similarity metric may produce largersimilarity metrics when two time series match more closely. Regardlessof how the similarity metric is calculated, step 1010 can includeidentifying the similarity metric which indicates the closest matchbetween the actuation signal time series t(x) and the correspondingsensor response time series r(x).

If the actuation device has the same similarity metric with multiplesensors (e.g., d(T₁, R₁)=d(T₁, R₂)), step 1010 can include examining thetime delay Δw between the actuation signal time series t(x) associatedwith the actuation device and sensor response time series r(x)associated with each of the sensors. The time delay Δw may indicate thedelay between the time w₁ at which the actuation signal is applied tothe actuation device and the time w₂ at which the effects of theactuation signal are evident in each sensor response (e.g., Δw=w₂−w₁).Step 1010 can include identifying the sensor and/or sensor response timeseries r(x) with the lowest time delay Δw relative to the actuationsignal time series t(x).

Still referring to FIG. 10, process 1000 is shown to include using thedevice pairing to generate and provide control signals to the actuationdevice (step 1012). Step 1012 can include using the device pairing tocreate a feedback control loop for HVAC system 600. The feedback controlloop can receive a feedback signal from the identified sensor and canprovide a control signal to the actuation device. Step 1012 can includeusing the device pairing to define the sensor and actuation device inthe control loop. For example, step 1012 can include creating a controlloop which receives a feedback signal from the sensor in the devicepairing and provides a control signal to the actuation device in thedevice pairing. Step 1012 can include mapping the sensor readings fromthe sensor in the device pairing to the feedback signal in the controlloop. Similarly, step 1012 can include mapping the actuation signalsprovided to the actuation device in the device pairing to the controlsignal in the control loop.

Step 1012 can include using the feedback control loop to generateactuation signals for the actuation device. Step 1012 can include usingstate-based algorithms, extremum seeking control (ESC) algorithms,proportional-integral (PI) control algorithms,proportional-integral-derivative (PID) control algorithms, modelpredictive control (MPC) algorithms, or any other type of controlmethodology to generate the actuation signals for the actuation devicebased on the sensor readings. For example, if the sensor reading fromthe sensor indicates that the temperature of a particular building zoneis below a temperature setpoint for the building zone, step 1012 caninclude providing an actuation signal to the actuation device toincrease the amount of heating provided to the building zone.Advantageously, process 1000 can be performed to automatically establishrelationships between various actuation devices and sensors based on thedevice pairings to allow controller 602 to determine which of theactuation devices can be operated to affect a given sensor reading.

Configuration of Exemplary Embodiments

The construction and arrangement of the systems and methods as shown inthe various exemplary embodiments are illustrative only. Although only afew embodiments have been described in detail in this disclosure, manymodifications are possible (e.g., variations in sizes, dimensions,structures, shapes and proportions of the various elements, values ofparameters, mounting arrangements, use of materials, colors,orientations, etc.). For example, the position of elements can bereversed or otherwise varied and the nature or number of discreteelements or positions can be altered or varied. Accordingly, all suchmodifications are intended to be included within the scope of thepresent disclosure. The order or sequence of any process or method stepscan be varied or re-sequenced according to alternative embodiments.Other substitutions, modifications, changes, and omissions can be madein the design, operating conditions and arrangement of the exemplaryembodiments without departing from the scope of the present disclosure.

The present disclosure contemplates methods, systems and programproducts on any machine-readable media for accomplishing variousoperations. The embodiments of the present disclosure can be implementedusing existing computer processors, or by a special purpose computerprocessor for an appropriate system, incorporated for this or anotherpurpose, or by a hardwired system. Embodiments within the scope of thepresent disclosure include program products comprising machine-readablemedia for carrying or having machine-executable instructions or datastructures stored thereon. Such machine-readable media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer or other machine with a processor. By way of example,such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROMor other optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to carry or storedesired program code in the form of machine-executable instructions ordata structures and which can be accessed by a general purpose orspecial purpose computer or other machine with a processor. Combinationsof the above are also included within the scope of machine-readablemedia. Machine-executable instructions include, for example,instructions and data which cause a general purpose computer, specialpurpose computer, or special purpose processing machines to perform acertain function or group of functions.

Although the figures show a specific order of method steps, the order ofthe steps may differ from what is depicted. Also two or more steps canbe performed concurrently or with partial concurrence. Such variationwill depend on the software and hardware systems chosen and on designerchoice. All such variations are within the scope of the disclosure.Likewise, software implementations could be accomplished with standardprogramming techniques with rule based logic and other logic toaccomplish the various connection steps, processing steps, comparisonsteps and decision steps.

1-20. (canceled)
 21. A building system for a building, the buildingsystem comprising one or more memory devices configured to storeinstructions thereon that, when executed by one or more processors,cause the one or more processors to: operate an actuation device of thebuilding to affect a variable of the building; generate an actuationdata time series indicating operation of the actuation device; receive aplurality of sensor response signals from a plurality of sensors, theplurality of sensor response signals indicating an effect of theoperation of the actuation device; generate a plurality of sensor datatime series based on the plurality of sensor response signals; perform atime series analysis on the actuation data time series and the pluralityof sensor data time series to identify one sensor of the plurality ofsensors associated with the actuation device; and establish a devicepairing comprising the actuation device and the one sensor of theplurality of sensors based on the time series analysis.
 22. The buildingsystem of claim 21, wherein the device pairing defines a controlrelationship between the actuation device in the device pairing and theone sensor of the plurality of sensors; wherein the control relationshipindicates that the actuation device in the device pairing is operable tocontrol the variable measured by the one sensor of the plurality ofsensors in the device pairing.
 23. The building system of claim 21,wherein the instructions cause the one or more processors to: create afeedback control loop comprising the actuation device in the devicepairing and the one sensor of the plurality of sensors in the devicepairing; and use the feedback control loop to generate and providecontrol signals to the actuation device based on measurements receivedfrom the one sensor of the plurality of sensors.
 24. The building systemof claim 21, wherein the building system is a heating, ventilation, andair conditioning (HVAC) system comprising: the actuation deviceconfigured to control the variable in the building; the plurality ofsensors configured to measure the variable; and a building controllercommunicably coupled to the actuation device and the plurality ofsensors, wherein the building controller includes the one or more memorydevices and the one or more processors.
 25. The building system of claim21, wherein the instructions cause the one or more processors to performthe time series analysis by: determining a plurality of delay timesbased on the plurality of sensor data time series and the actuation datatime series, each of the plurality of delay times being a delay timebetween a sensor of the plurality of sensors and the actuation device;and identifying the one sensor of the plurality of sensors associatedwith the actuation device by identifying a minimum delay time of theplurality of delay times.
 26. The building system of claim 21, whereinthe instructions cause the one or more processors to: detect adimensional mismatch between the actuation data time series and aparticular sensor data time series; and correct the dimensional mismatchby modifying at least one of the actuation data time series and theparticular sensor data time series.
 27. The building system of claim 21,wherein the instructions cause the one or more processors to perform thetime series analysis by: determining one or more sensor coefficients foreach of the plurality of sensors based on the plurality of sensor datatime series; determining one or more actuation device coefficients basedon the actuation data time series; and identifying the one sensor of theplurality of sensors associated with the actuation device based on theone or more sensor coefficients and the one or more actuation devicecoefficients.
 28. The building system of claim 27, wherein theinstructions cause the one or more processors to perform the time seriesanalysis by determining the one or more sensor coefficients for each ofthe plurality of sensors and the one or more actuation devicecoefficients by applying a discrete cosine transformation (DCT) to eachof the plurality of sensor data time series and to the actuation datatime series, each DCT generating a plurality of DCT coefficients,wherein one or more actuation device DCT coefficients resulting from theDCT of the actuation data time series are the one or more actuationdevice coefficients and one or more sensor DCT coefficients resultingfrom the DCT of each of the plurality of sensor data time series are theone or more sensor coefficients; wherein the instructions cause the oneor more processors to perform the time series analysis by generating oneor more quantized sensor DCT coefficients for each of the plurality ofsensors by quantizing the one or more sensor DCT coefficients resultingfrom the DCT of each of the plurality of sensor data time series andgenerate one or more quantized actuator DCT coefficients by quantizingthe one or more actuation device DCT coefficients resulting from the DCTof the actuation data time series.
 29. The building system of claim 27,wherein the instructions cause the one or more processors to perform thetime series analysis by: generating one or more quantized sensorcoefficients for each of the plurality of sensors by quantizing the oneor more sensor coefficients of each of the plurality of sensors andgenerating one or more quantized actuation device coefficients byquantizing the one or more actuation device coefficients; for each ofthe plurality of sensor response signals, calculating a plurality ofsimilarity metrics indicating a similarity between the plurality ofsensors and the actuation device based on the one or more quantizedsensor coefficients and the one or more quantized actuation devicecoefficients; and identifying the one sensor of the plurality of sensorsassociated with the actuation device based on the plurality ofsimilarity metrics.
 30. The building system of claim 21, wherein theinstructions cause the one or more processors to: receive baselinesensor signals from each of the plurality of sensors, the baselinesensor signals indicating values of the variable during a time periodbefore the operation of the actuation device; and for each of thebaseline sensor signals, calculate a baseline similarity metricindicating a baseline similarity between one of the baseline sensorsignals and the actuation device.
 31. The building system of claim 30,wherein the instructions cause the one or more processors to: determinewhether a similarity metric indicating similarity between the one sensorof the plurality of sensors and the actuation device indicates a greatersimilarity than a particular baseline similarity metric calculated basedon a particular baseline sensor signal of the baseline sensor signals;and establish the device pairing in response to a determination that thesimilarity metric indicating the similarity between the one sensor ofthe plurality of sensors and the actuation device indicates the greatersimilarity than the particular baseline similarity metric calculatedbased on the particular baseline sensor signal.
 32. A method of abuilding for pairing building devices, the method comprising: operating,by one or more processing circuits, an actuation device of the buildingto affect a variable of the building; generating, by the one or moreprocessing circuits, an actuation data time series indicating operationof the actuation device; receiving, by the one or more processingcircuits, a plurality of sensor response signals from a plurality ofsensors, the plurality of sensor response signals indicating an effectof the operation of the actuation device; generating, by the one or moreprocessing circuits, a plurality of sensor data time series based on theplurality of sensor response signals; performing, by the one or moreprocessing circuits, a time series analysis on the actuation data timeseries and the plurality of sensor data time series to identify onesensor of the plurality of sensors associated with the actuation device;and establishing, by the one or more processing circuits, a devicepairing comprising the actuation device and the one sensor of theplurality of sensors based on the time series analysis.
 33. The methodof claim 32, wherein the device pairing defines a control relationshipbetween the actuation device in the device pairing and the one sensor ofthe plurality of sensors; wherein the control relationship indicatesthat the actuation device in the device pairing is operable to controlthe variable measured by the one sensor of the plurality of sensors inthe device pairing.
 34. The method of claim 32, further comprising:creating a feedback control loop comprising the actuation device in thedevice pairing and the one sensor of the plurality of sensors in thedevice pairing; and using the feedback control loop to generate andprovide control signals to the actuation device based on measurementsreceived from the one sensor of the plurality of sensors.
 35. The methodof claim 32, wherein performing, by the one or more processing circuits,the time series analysis comprises: determining a plurality of delaytimes based on the plurality of sensor data time series and theactuation data time series, each of the plurality of delay times being adelay time between a sensor of the plurality of sensors and theactuation device; and identifying the one sensor of the plurality ofsensors associated with the actuation device by identifying a minimumdelay time of the plurality of delay times.
 36. The method of claim 32,further comprising: detecting, by the one or more processing circuits, adimensional mismatch between the actuation data time series and aparticular sensor data time series; and correcting, by the one or moreprocessing circuits, the dimensional mismatch by modifying at least oneof the actuation data time series and the particular sensor data timeseries.
 37. The method of claim 32, wherein performing, by the one ormore processing circuits, the time series analysis comprises:determining one or more sensor coefficients for each of the plurality ofsensors based on the plurality of sensor data time series; determiningone or more actuation device coefficients based on the actuation datatime series; and identifying the one sensor of the plurality of sensorsassociated with the actuation device based on the one or more sensorcoefficients and the one or more actuation device coefficients.
 38. Themethod of claim 37, wherein performing, by the one or more processingcircuits, the time series analysis comprises determining the one or moresensor coefficients for each of the plurality of sensors and the one ormore actuation device coefficients by applying a discrete cosinetransformation (DCT) to each of the plurality of sensor data time seriesand to the actuation data time series, each DCT generating a pluralityof DCT coefficients, wherein one or more actuation device DCTcoefficients resulting from the DCT of the actuation data time seriesare the one or more actuation device coefficients and one or more sensorDCT coefficients resulting from the DCT of each of the plurality ofsensor data time series are the one or more sensor coefficients; whereinthe performing, by the one or more processing circuits, the time seriesanalysis comprises generating one or more quantized sensor DCTcoefficients for each of the plurality of sensors by quantizing the oneor more sensor DCT coefficients resulting from the DCT of each of theplurality of sensor data time series and generate one or more quantizedactuator DCT coefficients by quantizing the one or more actuation deviceDCT coefficients resulting from the DCT of the actuation data timeseries.
 39. The method of claim 37, wherein performing, by the one ormore processing circuits, the time series analysis comprises: generatingone or more quantized sensor coefficients for each of the plurality ofsensors by quantizing the one or more sensor coefficients of each of theplurality of sensors and generating one or more quantized actuationdevice coefficients by quantizing the one or more actuation devicecoefficients; for each of the plurality of sensor response signals,calculating a plurality of similarity metrics indicating a similaritybetween the plurality of sensors and the actuation device based on theone or more quantized sensor coefficients and the one or more quantizedactuation device coefficients; and identifying the one sensor of theplurality of sensors associated with the actuation device based on theplurality of similarity metrics.
 40. One or more computer readablestorage media configured to store instructions thereon that, whenexecuted by one or more processors, cause the one or more processors to:operate an actuation device of a building to affect a variable of thebuilding; generate an actuation data time series indicating operation ofthe actuation device; receive a plurality of sensor response signalsfrom a plurality of sensors, the plurality of sensor response signalsindicating an effect of the operation of the actuation device; generatea plurality of sensor data time series based on the plurality of sensorresponse signals; perform a time series analysis on the actuation datatime series and the plurality of sensor data time series to identify onesensor of the plurality of sensors associated with the actuation device;and establish a device pairing comprising the actuation device and theone sensor of the plurality of sensors based on the time seriesanalysis.